The electrification of the transport and heating sector is increasing load levels and investment requirements in distribution grids, creating a growing need for automated and forward-looking expansion planning. This paper presents a practical framework for automated multi-period grid expansion planning and applies it to a real medium-voltage grid area. The planning problem combines network models, load forecasts, technical constraints, reinforcement measures and a hierarchical objective function. A Genetic Algorithm generates candidate expansion paths, while AC load flow calculations assess normal-operation and N−1 security across multiple planning years. The influence of algorithm parameterization, forecast assumptions and available measure libraries on solution quality, computational effort and investment strategies is investigated systematically. The results confirm the suitability of Genetic Algorithms for this application, showing that the parameterization substantially affects both solution quality and runtime. Although the Ambitious Scenario for electrification increases overall investments, many reinforced assets remain consistent with the Reference Scenario, while investment timing changes more strongly. Restricting topology modifying measures generally raises costs, whereas limiting cable cross-sections reduces the modelled network investment costs by increasing the use of existing infrastructure. The findings highlight that meaningful automated planning requires current network models, representative forecasts, realistic measures and a comprehensive cost model.