The Sparrow Search Algorithm (SSA) faces significant limitations in multi-objective robotic gripper optimization, such as excessive computational time consumption, suboptimal solutions, and proneness to local optima. To tackle these challenges, this paper proposes a Mixed Strategy Sparrow Search Algorithm (MSSSA). This algorithm enhances optimization performance through three synergistic strategies: dynamic adjustment of producer proportions to balance global exploration and local exploitation, chaotic perturbation mechanisms to avoid premature convergence, and hybridization with the Grey Wolf Optimizer (GWO) to leverage social hierarchy for directional convergence. The experimental results obtained from the CEC2017 benchmark functions and robotic gripper case studies demonstrate that the MSSSA surpasses other algorithms in robustness and practical effectiveness for engineering applications.