Genetic algorithms have proven to be an effective tool for chemical space exploration . A main advantage is that the generation and curation of training data is not needed for such methods as molecules can be evaluated on the fly by quantum-methods. Stochastic crossover combined with quantum-method guided optimization means that GA based methods can be easier to interpret than the notorious black box machine learning based methods. In this study, we apply a genetic algorithm to search for alternatives to the hexa-iso-propyl-terphen(HIPT) substituents that make the catalyst have favourable reaction free energies for the last two catalytic steps. The GA discovered molecules are validated at the DFT level of theory with the TZVP basis set and PBE/B3LYP functionals. Furthermore, the goal is to obtain DFT calculated catalytic cycles for promising GA molecular candidates and from these determine promising substituents. The design of catalysts using generative models is still in its infancy and requires additional work to fully exploit the methods potential. This is a preliminary study, where we determine the feasibility of this ambitious goal. Thus, we do not include important design considerations such as the steric protection of the the Mo atom to avoid H + reduction or dimerization of the catalyst.