An auto-tuning method for a Delta robot’s P/PI cascade motion controller using multi-objective optimization algorithm is proposed. The implemented control structure consists of two controllers: A feedforward controller based on a model of the inverse dynamics of the robot, and a cascade P/PI controller to compensate for unmodeled effects. The auto-tuning is achieved in the sense of optimizing the control parameters in three stages. In the first stage, the feedback control parameters are optimized after neglecting the feedforward control term. The goal is to minimize the position error in tracking an excitation trajectory, which is used as well to identify the dynamic model parameters in the second stage. After that, the feedforward compensation term is computed offline based on the desired trajectory. In the final stage, the P/PI parameters are optimized again after adding the feedforward controller. Experimental results on an industrial 3-dof Delta robot validates the efficiency of the proposed method.