Evolutionary algorithms have been extensively used for numerous optimization problems with great success in the past years. They mimic nature's process of evolution to find solutions to a large range of mathematical problems. In this work a new strategy is suggested to introduce gender, defined by a characteristic of an individual, that is easy to implement in evolutionary algorithms, as long as they are based on evaluating a fitness function. The new method outperforms comparable evolutionary approaches without gender for all standard test problems considered. The present study shows that with increasing problem complexity the performance of this gender variant increases to more than double the success rates while keeping the computational effort at about the same level and still being clearly better for easy problems. Additionally, in the mean, the new method results in better fitness values for all presented cases.