The aim of this work is towards a better understanding of the effect of using constraint violations in guiding evolutionary search for nonlinear programming problems. Different penalty functions, based on constraint violations, create different search biases. However, this bias may be eliminated when treating the nonlinear programming problem as a multiobjective task. The different search behaviors are illustrated using a new artificial test function. The effectiveness of the multiobjective approach is also compared with the standard penalty function method on a number of commonly used benchmark problems. It is shown that in practice multiobjective methods are not an efficient or effective approach to constrained evolutionary optimization.