Creating ways for neural networks to evolve is an important goal in the field of artificial evolution. The main problem is how to encode the structure and properties of the neural network in the genome. If one overloads the genome with detailed network information, the evolutionary time increases prohibitively. If the genome is too simple, only simple problems can be solved. Since nature has found an efficient evolutionary solution to this problem, it is worth imitating the mechanisms by which biological neural nets are generated. In this article, a model is proposed in which artificial genomes increase the ability of axons to find, deteet, and connect to specific targets. Some initial simulation results for simple tasks are evolved, and the genetic tuning of the developmental processes of artificial evolution is discussed.