Multi-Objective Optimization focuses on optimizing conflicting objective functions simultaneously, balancing the trade-off between multiple criteria to determine a set of efficient solutions. Evolutionary Algorithms mimic the process of natural selection, using a population-based approach to evolve a set of potential solutions over multiple generations. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is one of the most influential algorithms in the field due to its efficiency, robustness, and ability to handle complex, non-linear problems without requiring gradient information. This work aims to investigate the performances of NSGA-II on the ZCAT test suite and provide a picture of the strengths and weaknesses of the algorithm. ZCAT problems are a set of scalable problems with peculiar Pareto fronts incorporating several characteristics in the search space of the problems such as separability, multimodality, deceptiveness, and bias. The implementations have been carried out on pymoo framework, representing a possible extension of the set of test problems already available in the library.