Evolutionary algorithms (EA) are well-suited for optimization problems with numerous objectives. The Multi-Objective Water Cycle Algorithm (MOWCA) is a comparatively recent approach for determining the Pareto-optimal set for multi-objective optimization problems, and the Sustainable Supplier Selection Problem (SSS) plays an essential part in the optimization of SSS problem. The purpose of this study is to examine the Pareto solutions to the SSS issue using the Multi-Objective Water Cycle Algorithm. Initially, a Multi-Objective Water Cycle Algorithm (MOWCA) encouraged by the water cycle process is presented. The mathematical programming for the SSS problem is then developed. The stated Multi-Objective Programming Problem (MOPP) is then analyzed using the MOWCA to get the pareto solutions. The suggested approach's efficiency is examined utilizing performance metrics such as Generational Distance, Reverse Generational Distance, Delta Metric, and Metric of Spacing. Finally, a comparison study with various recent metaheuristic methods is performed.