Multi-Objective Evolutionary Algorithms (MOEAs) have significantly advanced the domain of Multi-Objective Optimization (MOO), facilitating solutions for complex problems with multiple conflicting objectives. This review explores the historical development of MOEAs, beginning with foundational concepts in multi-objective optimization, basic types of MOEAs, and the evolution of Pareto-based selection and niching methods. Further advancements, including decom-position-based approaches and hybrid algorithms, are discussed. Applications are analyzed in established domains such as engineering and economics, as well as in emerging fields like advanced analytics and machine learning. The significance of MOEAs in addressing real-world problems is emphasized, highlighting their role in facilitating informed decision-making. Finally, the development trajectory of MOEAs is compared with evolutionary processes, offering insights into their progress and future potential.