This paper provides an overview of evolutionary algorithms (EAs) in multiobjective optimization. It discusses various approaches to handling multiple objectives, including non-Pareto and Pareto-based methods, and explores the challenges and potential of EAs in this context. The paper highlights the importance of fitness assignment and search strategies in multiobjective optimization, and presents several techniques for addressing these issues. It also discusses the impact of different fitness assignment strategies on the fitness landscape, and how these can influence the search process. The paper concludes with a discussion of future research directions in multiobjective evolutionary algorithms, emphasizing the need for further exploration of niche induction techniques, adaptive representations, and the integration of learning paradigms. The paper is a comprehensive review of the current state of research in multiobjective evolutionary algorithms, and provides a foundation for future work in this area.This paper provides an overview of evolutionary algorithms (EAs) in multiobjective optimization. It discusses various approaches to handling multiple objectives, including non-Pareto and Pareto-based methods, and explores the challenges and potential of EAs in this context. The paper highlights the importance of fitness assignment and search strategies in multiobjective optimization, and presents several techniques for addressing these issues. It also discusses the impact of different fitness assignment strategies on the fitness landscape, and how these can influence the search process. The paper concludes with a discussion of future research directions in multiobjective evolutionary algorithms, emphasizing the need for further exploration of niche induction techniques, adaptive representations, and the integration of learning paradigms. The paper is a comprehensive review of the current state of research in multiobjective evolutionary algorithms, and provides a foundation for future work in this area.