Freight transport is a major contributor to Europe’s greenhouse gas emissions, a trend expected to intensify with rising delivery demands. Multimodal transport has gained attention to reduce emissions as electrification of road freight transport is progressing slowly. However, identifying such multimodal routes remains a challenge for companies aiming to adapt their logistics operations. This paper presents a graph-based route optimization tool for such multimodal routes based on road and rail data from OpenStreetMap. Intermodal stations are modeled as connection nodes between the two networks, and edge weights are defined according to multiple evaluation criteria. Four optimization objectives are investigated: distance, time, cost, and emission. Dijkstra’s algorithm is applied to compute optimal routes for each objective across the multimodal graph. Results show that the tool can provide feasible multimodal routes within practical execution times of around 40 s for a network covering all of Germany. Cost-based optimization tends to favor road transport due to comparatively low pricing, whereas emissions-based optimization consistently yields rail solutions. The study demonstrates the potential of combining open data with graph algorithms to support sustainable logistics planning and provides a foundation for further extensions to larger-scale and cross-border freight networks.