The share of electric vehicles is gradually increasing in the global vehicle market. Although electric vehicles have several advantages, they are characterised by relatively low cruising range (autonomy). Therefore, optimal battery utilization is a crucial question and challenge. Road traffic monitoring produces vast quantities of real time and historical data. Historical traffic data is a valuable resource in the field of traffic analysis and it constitutes the primary data used in this research. The aim of this research is to analyse road energy profiles from a spatio-temporal aspect and to develop a graphical user interface for integrating and illustrating the results of the research for end users. To achieve these aims we have implemented a methodology for estimating road energy profiles based on historical traffic data analysis. Furthermore, we have developed an approach for computing electric vehicle electricity consumption (or generation) on individual road segments. However, the values of the road’s energy profile change in the course of a day. Thus we have created an graphical user interface through which we can observe road energy profile values at a given time of day. We have also implemented an algorithm for clustering road segments based on similar energy profiles. Finally, we have developed an modified vehicle routing algorithm which dynamically calculates optimal routes based on road energy profiles at a given time of day. The results of this research can help inform policy makers on the given situation for electric vehicles implementation and proliferation in the larger urban area of Zagreb city. More importantly, combined with the graphical user interface and custom routing algorithm, the outcomes of this research could provide electric vehicle users a valuable tool for optimizing their vehicle utilization.