Multi-agent evolutionary reinforcement learning integrates evolutionary algorithms into multi-agent reinforcement learning, addressing inherent problems such as low-quality reward signals and non-stationarity. However, existing methods often struggle to balance learning and exploration between reinforcement learning and evolutionary algorithms. On one hand, suboptimal strategies in reinforcement learning can negatively affect the population. On the other hand, the low utilization of high-quality strategies within the population limits overall learning efficiency. Moreover, in complex partially observable environments, agents face challenges in obtaining effective observation representations, which reduces decision-making accuracy. To address these challenges, this study proposes an improved multi-agent evolutionary reinforcement learning method based on strategy optimization and representation search (SORS). First, to tackle the balance between learning and exploration, a reward-driven strategy optimization module is designed, utilizing superior strategies to guide population mutation in evolutionary algorithms and gradient optimization in reinforcement learning. Second, to mitigate partial observability in complex environments, a representation search method is introduced, enhancing agents’ observation representations by perturbing representation network populations. Finally, experiments conducted on a StarCraft simulation platform validate the proposed method. The results show that SORS achieves superior performance, surpassing all baseline algorithms in average win rates across different environments.