When we evaluate the search performance of an evolutionary computation (EC) technique, we usually apply it to typical benchmark functions and evaluate its performance in comparison to other techniques. In experiments on limited benchmark functions, it can be diffcult to understand the features of each technique. In this paper, the search spaces that emphasize the performance difference of EC techniques are evolved by Cartesian genetic programming (CGP). We focus on a real-coded genetic algorithm (RCGA), which is a type of genetic algorithm that has a real-valued vector as a chromosome. The performance difference of two RCGAs is assumed to be a objective function of CGP, and the search space that increases the performance difference is evolved. In particular, we generate search spaces using the performance difference of real-coded crossovers or generation alternation models. As a result of our experiments, the search spaces that exhibit the largest performance difference of two RCGAs are generated for all the combinations. In addition, we extend the objective functions to two of the performance differences and the number of active nodes in CGP and attempt to generate multiple search spaces with an evolution using a multiobjective evolutionary algorithm. We then observe which types of elements expand the performance difference.