As a preprocessing for genetic algorithms, static reordering helps genetic algorithms effectively create and preserve high-quality schemata, and consequently improves the performance of genetic algorithms. In this paper, we propose a static reordering method independent of problem-specific knowledge. One of the novel features of our reordering method is that it is applicable to any problem with no information about the problem. The proposed method constructs a weighted complete graph from the gene distances calculated from solutions with relatively high fitnesses, transforms them into a gene-interaction graph, and finds a gene rearrangement. Extensive experimental results showed significant improvement for a number of applications.