This article presents models and algorithms to simultaneously solve both the long-term grid planning and the distributed energy resource scheduling optimization problem for medium voltage grids. An emphasis is on evaluation of electric vehicle scheduling and demand side management. The main benefit of this new simultaneous optimization approach is that it converges towards a global optimum by considering not only infrastructure investments but also energy cost changes due to scheduling measures as for example curtailment or demand side management. The article firstly analyzes degrees of freedom in grid planning and in scheduling to derive models. The sections thereafter present the algorithm for simultaneous optimization. It integrates a fast scheduling optimization into a meta-heuristic grid-planning algorithm. The grid-planning algorithm uses Delaunay triangulation and an ant colony systems approach. The scheduling optimization combines dynamic programming and a fast heuristic to consider grid constraints. The last section presents exemplary results and illustrates effects of different regulatory regimes on costs. Results suggest that market-based scheduling will prevail due to energy cost-savings. However, purely market-based scheduling is unattractive when grid costs are considered. Distribution system operators can reduce long-term grid costs significantly, if they may perform demand side management.