In many real-world applications, mobile robots require interacting with objects in their environments by means of performing docking tasks in a precise manner. In the application domain of this work, an automated guided vehicle (AGV), specifically, a fork-lift truck must often perform docking maneuvers to load pallets in conveyor belts. The main purpose is to improve some features of docking task as its duration, accuracy and stability. We propose a soft computing technique based on a multi-objective evolutionary algorithm in order to find multiples fuzzy logic controllers which optimize specific objectives and satisfy imposed constraints for docking task in charge of following up an online generated trajectory.