This paper provides a systematic comparison of various evolutionary algorithms (EAs) for multiobjective optimization using six carefully chosen test functions. Each test function is designed to highlight specific challenges in the evolutionary optimization process, such as multimodality and deception. The study investigates the performance of different algorithms under these specific conditions to predict their suitability for certain problem types. The results indicate a hierarchy among the algorithms, with the Strength Pareto Evolutionary Algorithm (SPEA) showing the best performance. Elitism is found to be a crucial factor in improving the evolutionary search, particularly in preventing premature convergence. The paper also explores the impact of population size and elitism on the algorithms' performance, suggesting that larger populations and elitism can enhance convergence to the Pareto-optimal front. The study concludes that the suggested test functions provide sufficient complexity to compare multiobjective optimizers, and that combining promising aspects of different algorithms could lead to a more effective approach.This paper provides a systematic comparison of various evolutionary algorithms (EAs) for multiobjective optimization using six carefully chosen test functions. Each test function is designed to highlight specific challenges in the evolutionary optimization process, such as multimodality and deception. The study investigates the performance of different algorithms under these specific conditions to predict their suitability for certain problem types. The results indicate a hierarchy among the algorithms, with the Strength Pareto Evolutionary Algorithm (SPEA) showing the best performance. Elitism is found to be a crucial factor in improving the evolutionary search, particularly in preventing premature convergence. The paper also explores the impact of population size and elitism on the algorithms' performance, suggesting that larger populations and elitism can enhance convergence to the Pareto-optimal front. The study concludes that the suggested test functions provide sufficient complexity to compare multiobjective optimizers, and that combining promising aspects of different algorithms could lead to a more effective approach.