This thesis presents an approach to controlling Micromanagement in Real-Time Strategy (RTS) computer games using Potential Fields (PF) that are tuned with Multi-Objectve Optimized Evolutionary Algorithms (MOEA), specifically the Nondominated Sorting Genetic Algorithm (NSGA-II). The classic RTS title textit{StarCraft: Broodwar} has been chosen as testing platform due to its status in the competitive AI scene, the amount of detailed information available from previous research and projects, and the free open-source framework Brood War Application Programming Interface (BWAPI). The proposed AI controls its units by placing several types of Potential Fields onto the battlefield. The weights behind the PFs' calculations are optimized using NSGA-II. This work is an attempt to improve on previous methods done with PF in RTS. The results indicate that Multi-Objective Optimization is a suited method for optimizing Potential Fields in RTS games.