In complex engineering problems, it is common to deal with more than two output target variables, making it challenging to obtain the best trade-offs among all output variables. Multi-objective optimization algorithms are promising candidates for providing Pareto Fronts that describe these possibilities. Particularly in antenna design, the input variables are geometrical elements associated with the antenna type. On the other hand, the output variables are the desirable performance indicators, such as resonance frequency, bandwidth, and gain. This paper aims to use several state-of-the-art multi-objective evolutionary algorithms and study the underlying mechanics of their operators to understand how we can optimally choose the antenna design parameters. Moreover, we propose an entire pipeline to automate this task, which is based on main phases: performing simulations using six multi-objective evolutionary algorithms, analyzing the convergence, Pareto front approximation, and quality indicators. Numerical results demonstrate the OMOPSO is a potential approach for the two evaluated studies of cases on antenna design.