diff --git a/ema_workbench/em_framework/evaluators.py b/ema_workbench/em_framework/evaluators.py
index 18d208c8b..b8c8ec063 100644
--- a/ema_workbench/em_framework/evaluators.py
+++ b/ema_workbench/em_framework/evaluators.py
@@ -13,6 +13,7 @@
 import sys
 import threading
 import warnings
+import logging
 
 from ema_workbench.em_framework.samplers import AbstractSampler
 from .callbacks import DefaultCallback
@@ -419,10 +420,13 @@ def evaluate_experiments(self, scenarios, policies, callback, combine="factorial
 experiment_runner = None
 
 
-def mpi_initializer(models):
+def mpi_initializer(models, logger_level):
     global experiment_runner
     experiment_runner = ExperimentRunner(models)
 
+    # Configure logger based on the passed level and adjusted format
+    logging.basicConfig(level=logger_level, format="[%(processName)s/%(levelname)s] %(message)s")
+
 
 class MPIEvaluator(BaseEvaluator):
     """Evaluator for experiments using MPI Pool Executor from mpi4py"""
@@ -440,8 +444,12 @@ def initialize(self):
         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,))
+        self._pool = MPIPoolExecutor(initializer=mpi_initializer, initargs=(models, _logger.level))
         _logger.info(f"MPI pool started with {self._pool._max_workers} workers")
+        if self._pool._max_workers <= 10:
+            _logger.warning(
+                f"With only a few workers ({self._pool._max_workers}), the MPIEvaluator may be slower than the Sequential- or MultiprocessingEvaluator"
+            )
         return self
 
     def finalize(self):
@@ -456,18 +464,30 @@ def evaluate_experiments(self, scenarios, policies, callback, combine="factorial
         packed = [(experiment, experiment.model_name) for experiment in experiments]
 
         # Use the pool to execute in parallel
+        _logger.info(
+            f"MPIEvaluator: Starting {len(packed)} experiments using MPI pool with {self._pool._max_workers} workers"
+        )
         results = self._pool.map(run_experiment_mpi, packed)
 
+        _logger.info(f"MPIEvaluator: Completed all {len(packed)} experiments")
         for experiment, outcomes in results:
             callback(experiment, outcomes)
+        _logger.info(f"MPIEvaluator: Callback completed for all {len(packed)} experiments")
 
 
 def run_experiment_mpi(packed_data):
+    from mpi4py.MPI import COMM_WORLD
+
+    rank = COMM_WORLD.Get_rank()
+
     experiment, model_name = packed_data
+    _logger.info(f"MPI Rank {rank}: starting {repr(experiment)}")
 
     # Use the global ExperimentRunner created by the initializer
     outcomes = experiment_runner.run_experiment(experiment)
 
+    _logger.info(f"MPI Rank {rank}: completed {experiment}")
+
     return experiment, outcomes
 
 
diff --git a/ema_workbench/util/ema_logging.py b/ema_workbench/util/ema_logging.py
index 47538eb9e..678746ea9 100644
--- a/ema_workbench/util/ema_logging.py
+++ b/ema_workbench/util/ema_logging.py
@@ -178,7 +178,7 @@ def get_rootlogger():
     return _rootlogger
 
 
-def log_to_stderr(level=None):
+def log_to_stderr(level=None, set_root_logger_levels=False):
     """
     Turn on logging and add a handler which prints to stderr
 
@@ -206,4 +206,8 @@ def log_to_stderr(level=None):
     logger.addHandler(handler)
     logger.propagate = False
 
+    if set_root_logger_levels:
+        for _, mod_logger in _module_loggers.items():
+            mod_logger.setLevel(level)
+
     return logger