In recent years, several researchers deployed 'Pareto method' in multi-objective optimization of composite drilling. A set of optimal solutions (Pareto front) rather than a single optimal solution were obtained, hence an insight into trade-off between multiple objectives can be established. Sardinas et al. was the first to utilize such method, where GA algorithm was used to optimize two conflicting objectives in CF/epoxy drilling (material removal rate and delamination damage). Later, Wang et al. developed a method integrating BPNN, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and fuzzy c-means clustering to optimize the thrust force, delamination damage and production efficiency. Nine representative solutions were selected from the Pareto front for experiment validation and the model showed average errors of 3.21% and 1.11% for thrust force and delamination prediction, respectively. Despite these promising results, the large number of solutions present in the Pareto front remains a challenge for determining the most appropriate/satisfactory solution for experimental verification and practical implementation.