We introduce two novel evolutionary algorithms for the problem of optimising electricity distribution networks in remote areas. Traditionally, electricity networks have been built using a centralised network infrastructure. In remote areas these typically include long distances between sparsely distributed consumers that lead to high costs and several risks that accompany these long connection lines. Therefore, alternative distributed infrastructures including potential microgrids or stand-alone power systems can be beneficial. Given a set of electricity consumers, their loads and locations, the aim is to find an optimal electricity network topology that might not be interconnected. Previously, this problem has been addressed using different standard clustering approaches comparing the total network costs of each solution. We propose a problem-specific evolutionary algorithm that alters the network structure directly as well as a novel quadtree-based evolutionary algorithm. The algorithms minimise a non-linear objective function representing the cost of the network while approximating real-world constraints. They are tested on a large set of various problem instances and compared to the standard clustering algorithms that have been used previously and an intuitive baseline method. The problem-specific evolutionary algorithm outperforms most standard clustering methods on a range of different test cases while the quadtree-based algorithm is shown to be effective in certain settings.