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The utilization of meta-heuristics has been widespread in resolving optimization problems, with constant development of new and effective algorithms. Thisresearch presents the Garter Snake Optimization Algorithm (GSO), which ismotivated by the mating behavior of garter snakes and leverages various techniques such as screening, grafting, and annual growth to conduct a productive andthorough search of the exploration space. The algorithm incorporates both socialand personal behaviors to accomplish a balance between exploration and exploitation of the best solutions. To assess the performance of the proposed algorithm,it is tested on four restricted benchmark problems and a frequently utilized realengineering problem, and the optimization results are compared with other algorithms. In many respects, the GSO outperforms other algorithms, demonstratingits superiority and potential in addressing constrained optimization problems.