diff --git a/ema_workbench/em_framework/evaluators.py b/ema_workbench/em_framework/evaluators.py
index f0035b1bf..fc64b86fd 100644
--- a/ema_workbench/em_framework/evaluators.py
+++ b/ema_workbench/em_framework/evaluators.py
@@ -14,6 +14,8 @@
 import threading
 import warnings
 
+from mpi4py.futures import MPIPoolExecutor
+
 from ema_workbench.em_framework.samplers import AbstractSampler
 from .callbacks import DefaultCallback
 from .points import experiment_generator, Scenario, Policy
@@ -415,17 +417,29 @@ def evaluate_experiments(self, scenarios, policies, callback, combine="factorial
         add_tasks(self.n_processes, self._pool, ex_gen, callback)
 
 
+# Create a global ExperimentRunner that will be used by all the worker processes
+experiment_runner = None
+
+
+def mpi_initializer(models):
+    global experiment_runner
+    experiment_runner = ExperimentRunner(models)
+
+
 class MPIEvaluator(BaseEvaluator):
     """Evaluator for experiments using MPI Pool Executor from mpi4py"""
 
     def __init__(self, msis, **kwargs):
-        from mpi4py.futures import MPIPoolExecutor
-
         super().__init__(msis, **kwargs)
         self._pool = None
 
     def initialize(self):
-        self._pool = MPIPoolExecutor()
+        # Instead of instantiating the ExperimentRunner for each experiment, instantiate it once here
+        models = NamedObjectMap(AbstractModel)
+        models.extend(self._msis)
+
+        # Use the initializer function to set up the ExperimentRunner for all the worker processes
+        self._pool = MPIPoolExecutor(initializer=mpi_initializer, initargs=(models,))
         _logger.info(f"MPI pool started with {self._pool._max_workers} workers")
         return self
 
@@ -437,12 +451,8 @@ def evaluate_experiments(self, scenarios, policies, callback, combine="factorial
         ex_gen = experiment_generator(scenarios, self._msis, policies, combine=combine)
         experiments = list(ex_gen)  # Convert generator to list
 
-        # Create the model map just like in SequentialEvaluator
-        models = NamedObjectMap(AbstractModel)
-        models.extend(self._msis)
-
-        # Pack models with each experiment
-        packed = [(experiment, models) for experiment in experiments]
+        # Instead of sending all models for each experiment, send only the model_name
+        packed = [(experiment, experiment.model_name) for experiment in experiments]
 
         # Use the pool to execute in parallel
         results = self._pool.map(run_experiment_mpi, packed)
@@ -452,11 +462,10 @@ def evaluate_experiments(self, scenarios, policies, callback, combine="factorial
 
 
 def run_experiment_mpi(packed_data):
-    experiment, all_models = packed_data
-    model = all_models[experiment.model_name]
+    experiment, model_name = packed_data
 
-    runner = ExperimentRunner({experiment.model_name: model})
-    outcomes = runner.run_experiment(experiment)
+    # Use the global ExperimentRunner created by the initializer
+    outcomes = experiment_runner.run_experiment(experiment)
 
     return experiment, outcomes