This study applies a Genetic Algorithm(GA) to discover possible molybdenum based catalysts for nitrogen fixation with a fast semi-empirical quantum method (GFN2-xTB). The catalysts are restricted to follow two types of ligand coordination, one with 3 anionic ligands and 1 neutral ligand and the other with 3 anionic ligands and 2 neutral ligands. We use a dataset of 91 experimentally verified ligands as starting population for the GA and use a pre-screening procedure to assign coordinating atoms to candidate ligands and assign the ligand permutations in trigonal bipyramidal and octahedral configurations. The optimizing objective is the first protonation and reduction steps of the Schrock cycle and we find multiple candidates that appears at B3LYP level of theory to inherently improve upon these two first charge transfer steps while keeping the effect on other reaction barriers minimal.