Construction enterprises often undertake multiple projects simultaneously, necessitating the efficient allocation of shared resources while ensuring adherence to project deadlines. Addressing this challenge requires advanced optimization techniques to achieve resource balance. This study introduces an improved mountain gazelle optimizer (iMGO) incorporating opposition-based learning (OBL) mechanism to enhance search efficiency and solution diversity. By simultaneously evaluating candidate solutions and their opposite counterparts, iMGO mitigates premature convergence and optimizes the exploration–exploitation trade-off. A construction case study is used to validate the effectiveness of the proposed algorithm, demonstrating its superior performance in achieving optimal resource leveling compared to benchmark algorithms. Experimental results indicate that iMGO not only attains optimal solutions but also exhibits greater stability and consistency across multiple trials. These findings highlight the potential of the developed approach to enhance resource management efficiency in complex multi-project environments.