Abstract: To address issues such as low efficiency and premature convergence in robotic arm trajectory optimization, a robotic arm trajectory optimization algorithm based on the secretary bird optimization algorithm (SBOA) was proposed. In response to the shortcomings of traditional algorithms in terms of convergence speed and solution quality, this study introduces the Runge-Kutta computational principle and a joint opposition operator, incorporating chaotic sequences and a dynamic opposition-triggering mechanism to improve the algorithm's local search accuracy and global search capability. The optimization results are verified using the Siemens Process Simulate industrial simulation platform and compared with classical algorithms such as particle swarm optimization (PSO) and simulated annealing (SA). The results show that the hybird secretary bird optimization algorithm (HSBOA) has significant advantages in optimization accuracy, convergence speed, trajectory smoothness, and acceleration continuity. Simulation results demonstrate that HSBOA exhibits excellent stability and applicability in complex multi-objective optimization scenarios, meeting the high-precision and high-efficiency demands of industrial automation. This further validates its broad application potential in the field of industrial automation.