Millions of dollars are spent each year on water distribution systems. Pipe optimization techniques provide an opportunity for potential savings in costs for water supply systems. These optimization techniques include linear programming, non-linear programming, dynamic programming, enumerative approaches, and genetic algorithms. The former four techniques have been applied to pipe network optimization in the research literature over the last 20 years or so. Genetic algorithms provide a new approach to pipe network optimization. Details of this method are considered in this thesis. A global cost analysis of each component in a water supply system is carried out of the genetic algorithm in order to assess the form of the objective function. Traditionally, the analysis an pipeline systems has focused on the steady state. In the current work, a genetic algorithm optimization model, which uses a probabilistic search procedure that emulates Darwinian natural selection, is coupled with a transient hydraulic simulation model to generate and evaluate trial pipe network designs in search of an optimal solution. A new and powerful penalty function is also developed for pressure violations distinguishing steady state and transient conditions. The genetic algorithm search is applied to a case study which demonstrates its flexibility and the opportunity for significant cost saving offered by this method.