The time consumed by the palletizing robot during its running trajectory directly affects its work efficiency. An improved whale optimization algorithm is proposed to optimize the time for the trajectory planning of the palletizing robot. Based on the standard whale algorithm, chaotic mapping was used to initialize the population, and adaptive weights and improved convergence factors were introduced to improve the solution accuracy, convergence speed, and global search capability of the algorithm. Firstly, the kinematic model of the robot was developed based on the D-H parameter method; secondly, the path points through which the robot end-effector passes were planned in the joint space using a 3-5-3 mixed polynomial interpolation function; and then the time was optimized using the improved whale optimization algorithm. Finally, the effect was simulated and compared in MATLAB. The results show that the improved whale algorithm has higher solution accuracy and faster convergence compared with other similar algorithms. Combining the algorithm with trajectory optimization, the running time required for 3-5-3 polynomial trajectory planning without the algorithm optimization is reduced by 22.46%, and the trajectory of each joint is smooth and continuous, which verifies the effectiveness of the trajectory planning method.