Although solar energy represents one of the most available renewable and clean types of energy, its potential for electrical energy generation remains widely unutilized. In order to develop low-energy buildings for self-sustainable cities, reduce carbon missions or maximize passive solar heating, it is vital to increase solar energy utilization. The solar energy is normally actively converted into electricity using photovoltaic (PV) systems. They are commonly installed on buildings’ roofs, where it is generally considered that a surface oriented towards the equator with a tilt angle equal to a location’s latitude is optimal. However, this is often not the case due to local climatic conditions and influence of shadowing from terrain and man-made objects. Therefore, the optimal slope and orientation of a PV system attached onto a building’s surface presents an optimization issue for investors, as well as for architects, urban planners and civil engineers. In the presented research, a novel method for searching optimal solar building models within urban areas is proposed. The method’s input is the georeferenced LiDAR point cloud, where each point is classified as either building, terrain or vegetation. This cloud is then arranged into a regular 2.5D grid. In order to improve the accuracy of the solar potential estimation, the empty cells are interpolated. The method considers self-adaptive differential evolution (DE) for solving the constrained optimization problem. In the experiments, various strategies were tested and the best performer turned out to be the DE/best/1/bin strategy. For every candidate, a building was modelled on the grid and evaluated regarding solar potential by considering shadowing from real topographic data and local climate conditions. Rectangular, T and L-shaped buildings were considered with several design parameters, including position, building rotation, facades’ height, roof ’s height and slope, but the method is easily extensible to handle more building design parameters as well. The experiments confirmed that the method can efficiently find the solar building design with maximum solar potential within constrained optimisation space. To our knowledge, this is the first attempt to use LiDAR data in order to find the most efficient building design regarding the solar potential.