Long term planning of distribution networks falls into the group of combinatorial problems characterised by a large number of real and integer variables, multiple and mutually opposite criteria in goal function, large number of linear and non-linear restrictions, large number of the logic rules, uncertainty of input data and temporal dimension. Therefore the application of the conventional, intuitive and experience-based approaches to the planning is almost impossible. The application of traditional mathematical optimisation methods is conditioned by various approximations, not only due to nonlinearity of the goal function and restrictions, but also due to difficulties in mathematical formulation of restrictions and the rules of the existing practice. This doctoral dissertation proposes a new method based on the evolutionary algorithms aimed at dynamic planning of distribution networks of a number of spatially closed structures for an uncertain future. Based on the results of the planning, robust decisions are being made regarding the optimum capacities and locations of the new HV/MV substations, the sizes of the new transformers within the existing HV/MV substations and the routes of new conduits. Besides best actions to be realized, the optimal sequeance of the events and the best timing for realization of each action is provided. The used evolutionary algorithm operators enable the planning of the whole supply area into the given structure with minimum investment, maintenance costs, costs of power and energy losses and the costs of energy not supplied by a unique optimisation procedure. The model of the problem includes the existing network, uncertainty of input data and parameters, distributed generation, permissible voltage drop in regular and outage conditions, calculation of power and energy losses, verification of cable and transformer thermal limits and supply reliability indices. The procedure is based on a number of computer provided variant solutions, additionally evaluated by the planner, which are applicable to mid-term and long-term planning of distribution networks.