Supercapacitor Energy Storage Systems (SCESS) are widely used in urban rail transit for recovering regenerative braking energy from trains. With the expansion of their applications, optimizing the economic benefits of SCESS has become an important research topic. This paper focuses on the capacity configuration of SCESS and proposes a joint optimization approach that integrates train dwell time and storage capacity. Based on a system model of urban rail transit and modular configuration principles, the proposed method uses the payback period as the optimization objective and employs a Cheetah-based swarm intelligence algorithm to determine the optimal configuration. The approach is validated using operational data from a real-world urban rail line in China. Case study results show that the joint optimization strategy reduces the payback period from 6.75 years to 5.01 years, compared to optimizing SCESS capacity alone, demonstrating a significant improvement in economic performance. Furthermore, when compared with other metaheuristic algorithms such as the Genetic Algorithm and Particle Swarm Optimization, the proposed method achieves better optimization efficiency and effectiveness.