Compared to base aluminum alloys, the surface composites of aluminum alloys are more widely used in the automotive, aerospace, and other industries. The ability to yield enhanced physical properties and a smoother microstructure has made friction stir processing (FSP) the method of choice for developing aluminum-based surface composites in recent times. In this work, the Goal Programming (GP) approach is adopted for the Multi-Objective Optimization of FSP processes with three Artificial Intelligence (AI)-based metaheuristics, viz., Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Teaching–Learning-Based Optimization (TLBO). Three parameters, copper percentage (Cu%), graphite percentage (Gr%), and number of passes, are considered, and multi-factor non-linear regression prediction models are developed for the three responses, Tool Vibrations, Power Consumption, and Cutting Force. The TLBO algorithm outperformed the ABC and PSO algorithms in terms of solution quality and robustness, yielding significant improvements in tool life. The results with TLBO were improved by 20% and 14% compared to the PSO and ABC algorithms, respectively. This proves that the TLBO algorithm performed better compared with the ABC and PSO algorithms. However, the computation time required for the TLBO algorithm is higher compared to the ABC and PSO algorithms. This work has opened new avenues towards applying the GP approach for the Multi-Objective Optimization of FSP tools with composite parameters. This is a significant step towards toll life improvement for the FSP of composite alloys, contributing to sustainable manufacturing.