Objective Aiming at the limitations of low operating efficiency, high energy consumption, and poor stability in traditional trajectory planning of Delta robots during food sorting, this study proposes an optimized trajectory scheme to effectively reduce operation time, lower energy consumption, and improve operation stability.Methods On the basis of the architecture of the food sorting system, a trajectory optimization method for the Delta robot is proposed, which combines quintic uniform B-spline curves with multi-objective optimization. Firstly, the trajectory is planned using quintic uniform B-spline curves to minimize motion impact. Subsequently, a multi-objective optimization model integrating time, energy consumption and stability is constructed. This model is solved using an improved multi-objective particle swarm optimization algorithm, which incorporates adaptive weight adjustment and crowding ranking optimization, to obtain the optimal trajectory parameters. Finally, an experimental platform is constructed to verify the effectiveness of the proposed method.Results Compared with conventional methods, the experimental method reduces robot operation time by over 9.50%, decreases energy consumption by over 9.00%, and improves stability by over 18.00%. Meanwhile, it maintains an actual sorting accuracy above 97.50%, with an average sorting time of less than 0.80 seconds.Conclusion The proposed method achieves multi-objective collaborative optimization of operation time, energy consumption, and stability, effectively improving the operation performance and stability of Delta robots in food sorting scenarios.