Fiber-metal laminates (FMLs) possess significant design flexibility owing to the anisotropic nature of composite materials and their layered structure, making optimization studies essential for enhancing mechanical performance and achieving lightweight objectives. To enhance the impact resistance of FMLs, a novel stacking sequence optimization method was proposed based on a grey wolf optimizer (GWO)-categorical boosting (CatBoost) surrogate model. A four-factor, four-level full factorial design was adopted, using ply angles as design variables with energy absorption and peak force as dual objectives. Parametric modeling generated sample points for establishing a CatBoost surrogate model that captured nonlinear variable-target relationships. The GWO was integrated to optimize CatBoost hyperparameters, enabling efficient laminate impact performance prediction. The stacking sequence of the laminate was then optimized using a genetic algorithm (GA) with dual objectives: maximizing energy absorption and minimizing peak force. The results demonstrate that the GWO-CatBoost surrogate model achieves high-fidelity prediction of laminate impact performance, effectively replacing computationally expensive finite element analysis (FEA) while significantly improving computational efficiency. The optimization outcomes are further validated through explicit dynamic simulations using ABAQUS/Explicit. The optimized laminate demonstrates an 18.04% reduction in peak impact force and a 3.77% improvement in energy absorption under the optimal layup angles, providing a practical reference for engineering design applications.