This paper presents a systematic comparison of six multiobjective evolutionary algorithms (EAs) using six carefully chosen test functions. Each test function is designed to highlight specific challenges in multiobjective optimization, such as multimodality, deception, and non-convexity. The study investigates how different algorithms perform on these problem features and identifies the effectiveness of various techniques in finding Pareto-optimal solutions. The results show a hierarchy among the algorithms, with SPEA (Strength Pareto Evolutionary Algorithm) performing best, followed by NSGA (Nondominated Sorting Genetic Algorithm), VEGA, HLGA, NPGA, and FFGA. Elitism is shown to be an important factor in improving the performance of multiobjective EAs. The study also highlights the importance of population size and the need for a diverse population to avoid premature convergence. The results indicate that the suggested test functions provide sufficient complexity to compare different multiobjective optimizers, and that the performance of algorithms can vary significantly depending on the problem features and parameter settings. The paper concludes that the integration of elitism and nondominated sorting can lead to better performance in multiobjective optimization.This paper presents a systematic comparison of six multiobjective evolutionary algorithms (EAs) using six carefully chosen test functions. Each test function is designed to highlight specific challenges in multiobjective optimization, such as multimodality, deception, and non-convexity. The study investigates how different algorithms perform on these problem features and identifies the effectiveness of various techniques in finding Pareto-optimal solutions. The results show a hierarchy among the algorithms, with SPEA (Strength Pareto Evolutionary Algorithm) performing best, followed by NSGA (Nondominated Sorting Genetic Algorithm), VEGA, HLGA, NPGA, and FFGA. Elitism is shown to be an important factor in improving the performance of multiobjective EAs. The study also highlights the importance of population size and the need for a diverse population to avoid premature convergence. The results indicate that the suggested test functions provide sufficient complexity to compare different multiobjective optimizers, and that the performance of algorithms can vary significantly depending on the problem features and parameter settings. The paper concludes that the integration of elitism and nondominated sorting can lead to better performance in multiobjective optimization.