Methodologies to design efficient, affordable, and sustainable catalysts have advanced rapidly in recent years. With advances in computational power and the rapid development of computational methods, the scientific community is turning to material simulations to investigate the hidden potential of a plethora of possibly undiscovered materials in incredibly short timeframes, usually impossible via trial-and-error experimental approaches. This short review article provides an overview of evolutionary-based optimization techniques with a special focus on Genetic Algorithms (GA’s) and their potential use in the catalyst design process. The ‘descriptors’ required to design catalysts via evolutionary-based optimization techniques are discussed explicitly for five key chemical reactions, namely, the Oxygen Evolution Reaction (OER), the Oxygen Reduction Reaction (ORR), the Hydrogen Evolution Reaction (HER), the Nitrogen Reduction Reaction (NRR), and the CO2 Reduction Reaction (CO2RR). The descriptors and their appraisal discussed in this review will facilitate researchers using evolutionary-based optimization techniques for  catalyst design and discovery.