In conclusion, this work presents a genetic algorithm for in silico catalyst discovery of nitrogen fixation catalysts by searching chemical space for replacements to the HIPT substituent on the Schrock catalyst. From an extract of the ZINC database of 250K molecules, a Genetic Algorithm based workflow with the GFN2-xTB quantum method was used to discover 299 possible ligand candidates that went through a series of DFT validation steps which resulted in a final pool of 20 ligands for which full PBE-optimized catalytic cycles were obtained. These ligands were observed to lower energies for crucial reaction steps at both the xTB and the B3LYP level of theory. For one scoring function other sub-reactions energies were increased to a minor degree by the introduction of new ligands, for the remaining two scoring functions, other sub-reaction energies were severely increased by the HIPT substituent replacement. The structures and energy profiles of one promising ligand from each scoring function were examined in greater detail. Each of the three GA evolved ligands were seen to lower the reaction energies for the particular scoring step they were evaluated on. Thus emphasizing the capabilities of the genetic algorithm. The disparity of the ligands from different scoring functions and the varying degree for which they were able to effectively catalyze all sub-reactions highlights the importance of the choice of scoring function. It became evident that scoring on a single reaction step in some cases was a lacking approach as barriers were introduced for other sub-reactions in the catalytic cycle.