Abstract. Multi-objective optimization has been studied for over a century. For the past two decades, a new approach based on the principle of natural evolution called evolutionary multi-objective optimization (EMO) is popular and commonly used to solve multi-objective optimization problems. In EMO, approaches are based on evolutionary algorithms, where a population of trial solutions is used and evolved using the principles of natural evolution to obtain a set of solutions as close as possible to the Pareto optimal solutions. Due to the ability of EMO algorithms to generate multiple solutions in a single run and their versatility in handling dierent types of multi-objective optimization problems, they have attracted a lot of attention in the recent past. In this talk, we shall discuss the principles of EMO and commonly used EMO algorithms. Additionally, we shall discuss recent trends in EMO research.