Water distribution networks (WDNs) are vital for communities, facing threats like climate change and aging infrastructure. Optimizing WDNs for energy and water savings is challenging due to their complexity. In particular, pump scheduling stands out as a fundamental tool for optimizing both resources. Metaheuristics such as evolutionary algorithms (EAs) offer promising solutions, yet encounter limitations in robustness, parameterization, and applicability to real-sized networks. The encoding of decision variables significantly influences algorithm efficiency, an aspect frequently overlooked in the literature. This study addresses this gap by comparing solution representations for a multiobjective pump scheduling problem. By assessing metrics such as execution time, convergence, and diversity, it identifies effective representations. Embracing a multiobjective approach enhances comprehension and solution robustness. Through empirical validation across case studies, this research contributes insights for the more efficient optimization of WDNs, tackling critical challenges in water and energy management. The results demonstrate significant variations in the performance of different solution representations used in the literature. In conclusion, this study not only provides perspectives on effective pump scheduling strategies but also aims to guide future researchers in selecting the most suitable representation for optimization problems.