Evolutionary computation algorithms have emerged as powerful global optimization strategies for re-parametrizing semiempirical methods. In the context of QM/MM simulations on enzymatic systems, Nam et al. utilized genetic algorithms to develop a semiem-pirical AM1/d-PhoT Hamiltonian specifically parameterized for hydrogen, oxygen, and phosphorus atoms, aiming to accurately model phosphoryl transfer reactions catalyzed by enzymes and ribozymes. In this way, recent studies have employed single-objective microgenetic algorithms to optimize semiempirical methods by matching atomic forces to higherlevel theoretical calculations based on reaction path data. While these approaches have successfully improved the description of condensed-phase systems and reaction pathways, their application to enzymatic reactions has been notably limited. The majority of studies have concentrated on simple model systems, leaving unaddressed the complex challenges inherent in enzymatic mechanisms. These challenges are significant: enzymatic reactions typically involve multiple interacting species, undergo substantial electronic reorganizations, and are heavily influenced by their environment, necessitating the simultaneous optimization of diverse molecular properties. Current semiempirical parametrizations often fall short in accurately describing these biochemical processes, particularly those within large chemical environments. This limitation underscores the pressing need for developing parametrization approaches specifically designed to address the complexities of enzymatic reactions.