The paper introduces a new multiobjective evolutionary algorithm (MOEA) called NSGA-II, which addresses the computational complexity, lack of elitism, and the need for specifying a sharing parameter in existing nondominated sorting-based MOEAs. NSGA-II features a fast nondominated sorting approach with computational complexity $O(MN^2)$ and a selection operator that combines the parent and offspring populations to create a mating pool, selecting the best $N$ solutions based on fitness and spread. The algorithm is compared with two other elitist MOEAs, PAES and SPEA, on various test problems, showing better performance in terms of solution diversity and convergence to the true Pareto-optimal front. Additionally, NSGA-II is modified to handle constrained multiobjective optimization problems, demonstrating improved performance over other constraint-handling methods. The paper also discusses the impact of different parameter settings on NSGA-II's performance and evaluates its ability to handle rotated problems, where interactions among decision variables introduce additional challenges. Overall, NSGA-II is shown to be a robust and efficient MOEA for multiobjective optimization.The paper introduces a new multiobjective evolutionary algorithm (MOEA) called NSGA-II, which addresses the computational complexity, lack of elitism, and the need for specifying a sharing parameter in existing nondominated sorting-based MOEAs. NSGA-II features a fast nondominated sorting approach with computational complexity $O(MN^2)$ and a selection operator that combines the parent and offspring populations to create a mating pool, selecting the best $N$ solutions based on fitness and spread. The algorithm is compared with two other elitist MOEAs, PAES and SPEA, on various test problems, showing better performance in terms of solution diversity and convergence to the true Pareto-optimal front. Additionally, NSGA-II is modified to handle constrained multiobjective optimization problems, demonstrating improved performance over other constraint-handling methods. The paper also discusses the impact of different parameter settings on NSGA-II's performance and evaluates its ability to handle rotated problems, where interactions among decision variables introduce additional challenges. Overall, NSGA-II is shown to be a robust and efficient MOEA for multiobjective optimization.