Tungsten heavy alloys (WHAs) are gaining popularity in defense applications for the development of kinetic energy penetrators owing to their high density, strength, and ductility. In order to improve the quality and reduce the production cost, there is a need to use machining as secondary operation. WHAs, however, are difficult to machine. Therefore, a solution that establishes the best cutting parameters to obtain the desired output with the fewest number of experimental trials while maintaining the accuracy of forecasted outcomes is required. In present study, a unique approach has been proposed that combines the numerical and analytical approaches with the evolutionary algorithms in order to develop multi-objective optimization models based on full factorial design data with a minimum amount of experimental tests. Three different evolutionary algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II), Hybrid Artificial Bee Colony algorithm (HABC), and Hybrid Cuckoo Search algorithm (HCS), were used to predict the optimum cutting parameters. NSGA-II was used as a benchmark among the three algorithms, while the other two were newly proposed. The best optimization strategy was proposed after the optimum cutting parameters predicted by three algorithms were validated with confirmation experiments. The proposed algorithm HCS was found to perform well for all output responses with an error variation of 3–15%.