Optimization of process parameters is important to achieving high quality in the machining process, especially where more complex multiple performance optimization is required. The present investigation focuses on the multiple performance optimization on end milling characteristics of LM25 Al/SiCp metal matrix composites. The process parameters used for the experiments were spindle speed, feed rate, depth of cut, and percentage weight of silicon carbide. Experiments were carried out according to response surface methodology (RSM). Statistical models were developed for tool flank wear and surface roughness. These models were used for optimization by which the optimum parameter settings were obtained with a view to minimizing the responses. The Non-dominated Sorting Genetic Algorithm (NSGA-II) tool was used to optimize the cutting conditions, yielding a non-dominated solution set that is reported here.