Due to the forthcoming regulation schemes throughout Europe, new challenges for natural gas network operators arise. The pressure for realizing and operating cost-efficient network structures increases as the regulation is based on a comparison of different network operators with the network operator setting the minimal allowable costs. Optimization methods, which will also be applied by ... [Show full abstract] regulatory authorities as part of the analytical cost models for calculating the efficiency of natural gas networks, provide the opportunity to identify long-term cost-efficient network structures, so called reference networks. Boundary conditions of natural gas networks, which concern the system’s technical safety and thus need to be regarded during network planning, are given by the rules set by each country’s technical assembly for natural gas supply. Degrees of freedom exist in alternative network structures, the number and degree of pressure stages and for the dimensioning of equipment. Therefore, optimization methods are required for solving the extensive optimization problem. Especially heuristic optimization algorithms have proved to deliver an optimal performance for the determination of cost-efficient network structures. Their essential advantages over exact methods are a reduced computational effort, leading to computing times of typically few hours for real natural gas systems while simultaneous delivering several similar cost-efficient network structures. These advantages allow sensitivity analysis by a variation of boundary conditions and supply tasks on network structure and network costs and lead to a greater flexibility for the future network development. Therefore, an optimization method for natural gas distribution networks based on Genetic Algorithms is proposed. The method is capable of calculating cost-efficient network structures with regard to all technical and economic boundary conditions and is also used by the German Federal Network Agency for calculating reference networks with minimum costs for given supply tasks. Exemplary applications demonstrate the method’s capability and the advantages through applying this method for long-term planning of natural gas networks.