The increasing prevalence of distributed photovoltaic (PV) units raises stress on distribution grids and necessitates increased grid planning efforts. We present a decision support system (DSS) based on integer programming that is able to determine cost-optimal grid reinforcements at the level of individual grid segments. The functionality of the DSS is demonstrated in a scenario analysis of a rising adoption of PV units relying on 1,000 simulation runs in a real-world grid. Based on the results, we provide guidelines for operative grid planning and illustrate how the system assists in the evaluation of reinforcement technologies as well as in long-term investment planning. Furthermore, thanks to segment-specific optimization, the DSS shows that at constant adoption levels, reinforcement cost can vary largely depending on the location of the PV units in the grid. Therefore, a high amount of uncertainty seems unavoidable in long-term prognoses on the effects of solar power on distribution grids.