diff --git a/Examples/Scripts/Optimization/Optuna_tuning.py b/Examples/Scripts/Optimization/Optuna_tuning.py
index f99c827933c..f4e26342f11 100644
--- a/Examples/Scripts/Optimization/Optuna_tuning.py
+++ b/Examples/Scripts/Optimization/Optuna_tuning.py
@@ -79,7 +79,7 @@ def __init__(self, k_dup, k_time):
         self.k_dup = k_dup
         self.k_time = k_time
 
-    def __call__(self, trial):
+    def __call__(self, trial, ckf_perf=True):
         params = []
 
         maxSeedsPerSpM = trial.suggest_int("maxSeedsPerSpM", 0, 10)
@@ -109,30 +109,7 @@ def __call__(self, trial):
             "deltaRMax",
         ]
 
-        outputDir = Path(srcDir / "Output_CKF")
-        outputfile = srcDir / "Output_CKF/performance_ckf.root"
-        outputDir.mkdir(exist_ok=True)
-        run_ckf(params, keys, outputDir)
-        rootFile = uproot.open(outputfile)
-        self.res["eff"].append(rootFile["eff_particles"].member("fElements")[0])
-        self.res["fakerate"].append(rootFile["fakerate_tracks"].member("fElements")[0])
-        self.res["duplicaterate"].append(
-            rootFile["duplicaterate_tracks"].member("fElements")[0]
-        )
-
-        timingfile = srcDir / "Output_CKF/timing.tsv"
-        timing = pd.read_csv(timingfile, sep="\t")
-        time_ckf = float(
-            timing[timing["identifier"].str.match("Algorithm:TrackFindingAlgorithm")][
-                "time_perevent_s"
-            ]
-        )
-        time_seeding = float(
-            timing[timing["identifier"].str.match("Algorithm:SeedingAlgorithm")][
-                "time_perevent_s"
-            ]
-        )
-        self.res["runtime"].append(time_ckf + time_seeding)
+        get_tracking_perf(self, ckf_perf, params, keys)
 
         efficiency = self.res["eff"][-1]
         penalty = (
@@ -144,6 +121,49 @@ def __call__(self, trial):
         return efficiency - penalty
 
 
+def get_tracking_perf(self, ckf_perf, params, keys):
+    if ckf_perf:
+        outDirName = "Output_CKF"
+        outputfile = srcDir / outDirName / "performance_ckf.root"
+        effContName = "particles"
+        contName = "tracks"
+    else:
+        outDirName = "Output_Seeding"
+        outputfile = srcDir / outDirName / "performance_seeding.root"
+        effContName = "seeds"
+        contName = "seeds"
+
+    outputDir = Path(srcDir / outDirName)
+    outputDir.mkdir(exist_ok=True)
+    run_ckf(params, keys, outputDir)
+    rootFile = uproot.open(outputfile)
+    self.res["eff"].append(rootFile["eff_" + effContName].member("fElements")[0])
+    self.res["fakerate"].append(rootFile["fakerate_" + contName].member("fElements")[0])
+    self.res["duplicaterate"].append(
+        rootFile["duplicaterate_" + contName].member("fElements")[0]
+    )
+
+    timingfile = srcDir / outDirName / "timing.tsv"
+    timing = pd.read_csv(timingfile, sep="\t")
+
+    if ckf_perf:
+        time_ckf = float(
+            timing[timing["identifier"].str.match("Algorithm:TrackFindingAlgorithm")][
+                "time_perevent_s"
+            ]
+        )
+
+    time_seeding = float(
+        timing[timing["identifier"].str.match("Algorithm:SeedingAlgorithm")][
+            "time_perevent_s"
+        ]
+    )
+    if ckf_perf:
+        self.res["runtime"].append(time_ckf + time_seeding)
+    else:
+        self.res["runtime"].append(time_seeding)
+
+
 def main():
     k_dup = 5
     k_time = 5
diff --git a/Examples/Scripts/Optimization/Orion_tuning.py b/Examples/Scripts/Optimization/Orion_tuning.py
index fd7b91caee9..57942b0763e 100644
--- a/Examples/Scripts/Optimization/Orion_tuning.py
+++ b/Examples/Scripts/Optimization/Orion_tuning.py
@@ -32,6 +32,8 @@
 
 from orion.client import build_experiment
 
+from Optuna_tuning import get_tracking_perf, run_ckf
+
 srcDir = Path(__file__).resolve().parent
 
 
@@ -82,6 +84,7 @@ def __call__(
         maxPtScattering,
         deltaRMin,
         deltaRMax,
+        ckf_perf=True,
     ):
         params = [
             maxSeedsPerSpM,
@@ -104,30 +107,7 @@ def __call__(
             "deltaRMax",
         ]
 
-        outputDir = Path(srcDir / "Output_CKF")
-        outputfile = srcDir / "Output_CKF/performance_ckf.root"
-        outputDir.mkdir(exist_ok=True)
-        run_ckf(params, keys, outputDir)
-        rootFile = uproot.open(outputfile)
-        self.res["eff"].append(rootFile["eff_particles"].member("fElements")[0])
-        self.res["fakerate"].append(rootFile["fakerate_tracks"].member("fElements")[0])
-        self.res["duplicaterate"].append(
-            rootFile["duplicaterate_tracks"].member("fElements")[0]
-        )
-
-        timingfile = srcDir / "Output_CKF/timing.tsv"
-        timing = pd.read_csv(timingfile, sep="\t")
-        time_ckf = float(
-            timing[timing["identifier"].str.match("Algorithm:TrackFindingAlgorithm")][
-                "time_perevent_s"
-            ]
-        )
-        time_seeding = float(
-            timing[timing["identifier"].str.match("Algorithm:SeedingAlgorithm")][
-                "time_perevent_s"
-            ]
-        )
-        self.res["runtime"].append(time_ckf + time_seeding)
+        get_tracking_perf(self, ckf_perf, params, keys)
 
         efficiency = self.res["eff"][-1]
         penalty = (