The transport sector contributes significantly to the EU's emissions, accounting for 20% of the total CO2 emissions 2022. Heavy-Duty Vehicles (HDV) are vital contributors, responsible for 15-22% of transportation emissions despite being less than 5% of total traffic. To meet the EU's climate goals, they aim to reduce truck CO2 emissions by 30% by 2030. A proposed solution is to increase the number of electric vehicles and charging stations (CS). Several studies have been conducted where different approaches have evaluated how the distribution of CS should look like. However, no studies have evaluated the suitability of existing gas stations as locations for HDV CS compared to establishing stations at any location within the target road network. Therefore, this study compares the effectiveness of an optimized selection of 25, 50, and 100 CS locations among the 2252 currently existing gas station locations that are along the target road network and the effectiveness of an optimized selection of an equal number of CS locations at any (unrestricted) location along the same target road network. Both cases involve highly optimized selection processes. The study evaluates different electrification effects, including transport work on station charge, station demand, fully electric transport work, fully electric transport distance, and fully electric volume. This analysis used Gordian Logistics Optimization Systems’ analytic service API, a data-driven spatial decision support tool for charging infrastructure planning. The tool optimally places CS within a network, evaluating the network effects related to electrification metrics. Data from open sources were used, and results and spatial patterns were visualized and calculated using ArcGIS Pro, a GIS platform.The study shows minimal differences in electrification effects, estimated revenue, and reduced CO2 emissions between placing CS at existing gas stations and freely optimized locations, with spatial distributions for these optimized locations often coinciding. However, the study also reveals variability in the number of charging points each CS needs, indicating that some projected CS power requirements would exceed the power grid’s capacity. This suggests that not all gas stations should have CS, and those that do should have varying numbers of chargers. Additionally, estimating the number of required chargers based solely on local traffic indicators is imprecise, and the placement of chargers is not random. These findings emphasize the need for careful infrastructure planning and the value of using intelligent support systems for decision-making. This is particularly evident when comparing the projected total network demand from planned networks against 1,000 randomized networks, with the scheduled networks ranking in the 99th percentile, demonstrating the effectiveness of spatial decision support systems