Abstract: A strategy for optimal time trajectory planning of robotic arms based on the modified adaptive genetic particle swarm optimization (MAGA-PSO) algorithm is proposed to address the issue of local optima trapping and low operational efficiency commonly encountered in traditional Particle Swarm Optimization algorithms. Firstly, the inertia weight and learning factor of the Particle Swarm Optimization algorithm are adaptively adjusted to enhance the search efficiency of the algorithm. Secondly, crossover and mutation operations from the genetic algorithm are introduced to design adaptive crossover and mutation strategies, increasing particle diversity to prevent the algorithm from converging to local optima. Finally, a simulation model of the Handsfree v6 plus six-axis robotic arm is established using Matlab/Simulink. A 3-5-3 polynomial interpolation function is utilized to generate robotic arm trajectory and conduct simulation experiments. Experimental results demonstrate that the improved algorithm enhances the convergence speed and solution accuracy of the robotic arm, with a runtime approximately 17.5% shorter compared to the standard particle swarm optimization algorithm, effectively improving the operational efficiency of the robotic arm.