This paper presents an improved version of a simple evolution strategy (SES) to solve global nonlinear optimization problems. As its previous version, the approach does not require the use of a penalty function, it does not require the definition by the user of any extra parameter (besides those used with an evolution strategy), and it uses some simple selection criteria to guide the process to ... [Show full abstract] the feasible region of the search space. Unlike its predecessor, this new version uses a multimembered Evolution Strategy (ES) and an improved diversity mechanism based on allowing infeasible solutions close to the feasible region to remain in the population. This new version was validated using a well-known set of test functions. The results obtained are very competitive when comparing the proposed approach against the previous version and other approaches representative of the state-of-the-art in constrained evolutionary optimization. Moreover, its computational cost (measured in terms of the number of fitness function evaluations) is lower than the cost required by the other techniques compared.