diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py
index 2f0086ac..66122fe2 100644
--- a/src/jimgw/jim.py
+++ b/src/jimgw/jim.py
@@ -3,7 +3,6 @@
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline
 from flowMC.proposal.MALA import MALA
 from flowMC.Sampler import Sampler
-from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer
 from jaxtyping import Array, Float, PRNGKeyArray
 
 from jimgw.base import LikelihoodBase
@@ -119,31 +118,6 @@ def sample(self, key: PRNGKeyArray, initial_position: Array = jnp.array([])):
                 initial_position = initial_position.at[non_finite_index[:common_length]].set(guess[:common_length])
         self.sampler.sample(initial_position, None)  # type: ignore
 
-    def maximize_likelihood(
-        self,
-        bounds: Float[Array, " n_dim 2"],
-        set_nwalkers: int = 100,
-        n_loops: int = 2000,
-        seed=92348,
-    ):
-        key = jax.random.PRNGKey(seed)
-        set_nwalkers = set_nwalkers
-        initial_guess = self.prior.sample(key, set_nwalkers)
-
-        def negative_posterior(x: Float[Array, " n_dim"]):
-            return -self.posterior(x, None)  # type: ignore since flowMC does not have typing info, yet
-
-        negative_posterior = jax.jit(jax.vmap(negative_posterior))
-        print("Compiling likelihood function")
-        negative_posterior(initial_guess)
-        print("Done compiling")
-
-        print("Starting the optimizer")
-        optimizer = EvolutionaryOptimizer(self.prior.n_dim, verbose=True)
-        _ = optimizer.optimize(negative_posterior, bounds, n_loops=n_loops)
-        best_fit = optimizer.get_result()[0]
-        return best_fit
-
     def print_summary(self, transform: bool = True):
         """
         Generate summary of the run