A new evolutionary method of solving multi-objective optimization problems is presented. This method utilizes the information about an individual sex for the purpose of distinction between different groups of objectives. In particular, this information is extracted from the fitness of individuals and applied during the parental crossovers in a multi-objective optimization process. Characteristics of this mechanism are discussed and its performance in an exemplary multi-objective PID control optimization task is presented.