This research addresses the deployment challenge in Wireless Sensor Networks (WSNs) by introducing a novel hybrid metaheuristic algorithm that combines the Firefly Algorithm (FA) and the Grasshopper Optimization Algorithm (GOA). While FA has shown promise in solving technical problems, it suffers from slow convergence and limited local search capabilities, particularly in high-dimensional spaces. To mitigate these limitations, we propose the Firefly-Grasshopper Optimization Algorithm (FAGOA), which integrates the adaptive search dynamics of GOA to enhance FA’s exploitation phase. Experimental results demonstrate that FAGOA significantly improves deployment efficiency in WSNs by optimizing parameters such as energy consumption and coverage overlap. Comparisons with state-of-the-art algorithms reveal FAGOA’s superior performance.