In this paper, a greedy Genetic Algorithm for continuous variables electromagnetic optimization problems is presented, The presented algorithm is characterized by the use of a nonlinear simplex method as a principal optimizator, and of a greedy genetic algorithm to explore the search space, realizing a balance between diversity and a bias toward fitter individuals. The resulting algorithm merges the efficiency typical of calculus-based search with the robustness typical of random methods, A detailed comparison of performances obtained implementing several strategies is eventually presented, using an electromagnetic design test problem.