In homogeneous wireless sensor networks (HWSNs), optimizing the Maximal Exposure Path (MaEP) is essential for applications requiring efficient path coverage and monitoring. This paper introduces a multi-objective optimization approach for solving the MaEP problem by addressing two conflicting objectives: minimizing path length and maximizing exposure, known as MaEP-MOO. The proposed method applies a tailored multi-objective evolutionary algorithm (MOEA) to effectively balance these objectives, leveraging sensor homogeneity to enhance solution accuracy and computational efficiency. Simulation results demonstrate the approach’s capability to generate Pareto-optimal solutions, providing trade-offs between path length and exposure. This work contributes a novel framework for path optimization in HWSNs, advancing their application in areas such as environmental monitoring, security, and precision agriculture.