This work presents and compares three evolutionary algorithms (EAs), each one from three of the main trends in literature: genetic algorithm (GA), evolutionary programming (EP), and evolutionary strategies (ES). They are validated and applied to optimize microstrip antennas (MSA) design in single and multiobjective approaches. The effect of random numbers with different distributions (Uniform, Gaussian, Cauchy and hybrid Gaussian-Cauchy) used by genetic operators is analysed. A CAD Model based on Cavity Method is used to assess MSAs. Standing wave ratio (SWR), bandwidth (BW), and radiation efficiency (er) are optimized parameters. Average maximum values for fitness are obtained and compared fore each case. Through the multiobjective approach, each EA generates the Pareto Frontier for the problem. Finally, the ability of each algorithm to find dominants individuals is compared, what allows the assessment or their efficiency.