An empirical study about the features that prevent an Evolutionary Algorithm to reach the feasible region or even get the global optimum when it is used to solve global optimization constrained optimization problems is presented. For the experiments we use a Simple Multimembered Evolution Strategy which provides very competitive results in the well known benchmark of 13 test functions. Also, we add 11 new problems which have features we hypothesize that decrease the performance of the algorithm (nonlinear equality constraints and dimensionality).