Genetic algorithms are adaptive algorithms which find solutions to problems by an evolutionary process based on natural selection. They can be used to find approximate solutions to optimization problems in cases where finding the precise optimum is prohibitively expensive, or where no algorithm is known. This paper discusses the use of (nonstandard) genetic algorithms for solving an optimization problem for a communication network. In the implementation of the system, a graph representation of a solution of the problem was used, as opposed to the representations based on bit strings (as is done in most work on genetic algorithms). This work is also a part of a larger project to create a new programming environment to support all kinds of optimization problems.