Many real engineering optimization problems can be modeled as multi-objective optimization problem (MOP). These problems actually do have multiple objectives that conflict each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. Evolutionary algorithms (EAs) have emerged as flexible and robust meta-heuristic methods for solving optimization problems, achieving the high level of problem-solving efficacy spanning every application domain. The main motivation for using EAs to solve MOPs is because EAs deal simultaneously with a set of possible solutions. This allows us to find several members of the Pareto optimal set in a single run of the algorithm. Research works have ascertained that genetic algorithm (GA) and particle swarm optimization (PSO) find more suitability for solving complex and large constrained combinational optimization problems in engineering and scientific domains. Evolutionary techniques for multi-objective optimization are categorized into three approaches: plain aggregating approaches, population-based non-Pareto approaches, and Pareto-based approaches. This chapter provides an introduction to few, multi-objective evolutionary algorithms (MOEAs) based on GA and PSO. The algorithms described are based upon plain aggregating approaches and Pareto-based approaches. An insight into the several variants of the basic algorithm and how these algorithms could be hybridized with techniques like Hill Climbing for better performance is also described.