Microgrids, or MGs, use renewable energy sources to provide the increasing need for energy. On the other hand, operational issues come from intermittent renewables and low power quality. To solve these problems, heuristic optimization strategies have been created by researchers. However, addressing different operational parts of MG is challenging due to mistakes in non-linear and nonconvex optimization induced by local minima and the inability of heuristic approaches to attain a global minimum. The importance of meta-heuristic optimization algorithms (MHOAs) in enhancing MG operational performance is reviewed in this work. The principles of MG optimization are covered, together with current developments in load forecasting, MG techno-economic analysis, resilience enhancement, and energy management. Nearly 25% of research uses particle swarm optimization, while 10% and 5% use genetic and grey wolf algorithms. According to this outcome, MHOA offers a system-agnostic optimization strategy that opens up new possibilities for raising the efficacy of MGs in the future. Lastly, we draw attention to a few difficulties that arise when integrating MHOAs into MGs, which may inspire more study in this field.