Software testing plays a pivotal role in the software development life cycle, ensuring the quality and reliability of software systems. However, it is often a labor-intensive and time-consuming process. To address this challenge, researchers have explored various search-based automated techniques for generating test suites. This paper proposes a novel hybrid approach, termed GWPSO, which combines Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO), for test case generation. The objective is to improve the efficiency and effectiveness of test case generation by leveraging the strengths of both GWO and PSO algorithms. In this study, the Hybrid GWPSO approach is compared with Genetic Algorithm (GA), PSO, and GWO in generating test cases for five benchmark problems. The results demonstrate the superiority of the proposed Hybrid GWPSO approach, showing significant improvements in terms of test case generation effectiveness. The findings of this research contribute to advancing the field of automated test case generation and hold implications for enhancing software testing practices in real-world software development projects.