The probability of power outages increases as electricity demand increases. To solve this problem, utility companies have started purchasing electricity through e-auctions. We investigate a method that involves the acquisition of energy from several suppliers, balancing several goals, and solving a challenging winner-determination problem to maximize resource acquisition to meet increasing power demand. Using exact methods is impractical to solve these NP-hard problems. As an alternative, nature-inspired methods are more effective, as they can trade in the quality of the solution with the required computational time. Previous studies focused on the application of different nature-inspired techniques (Genetic Algorithms (GAs), Whale Optimization Algorithm (WOA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Firefly Algorithm (FA)) in terms of effectiveness in producing high-quality solutions for various instances of the Combinatorial Reverse Auction (CRA) problem. This study investigates the effectiveness, in solution quality and time performance, of variants of Multi-Objective Optimization (MOO) methods in tackling the CRA problem. These variants include the Strength Pareto Evolutionary Algorithm (SPEA) with different clustering techniques, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto-Dominance Evolutionary Algorithm (EA). Moreover, a comparison has been conducted between evolutionary-based single-objective and multiobjective approaches to solve instances of CRA problems.