This research addresses the challenge of maximizing social welfare in electricity markets by leveraging advanced optimization methodologies. Specifically, the study evaluates the performance of evolutionary algorithms (EAs) such as genetic algorithms (GA), differential evolution (DE), and nondominated sorting genetic algorithm II (NSGA-II) against the exact optimization benchmark provided by the branch and cut method. To enhance the effectiveness of these algorithms, a robust hyperparameter optimization (HPO) framework was employed, incorporating tree-structured Parzen estimator (TPE), covariance matrix adaptation evolution strategy (CMA-ES), and random search techniques. By systematically analyzing the balance between exploration and exploitation capabilities, this study identifies the strengths and limitations of each HPO method in fine-tuning the performance of the EAs. The results provide actionable insights into the optimal algorithmic configurations necessary for achieving superior social welfare outcomes in complex electricity market scenarios, thereby advancing both operational and economic efficiencies.