The global rise in electric vehicle (EV) adoption highlights the need for expanded charging infrastructure. A significant gap exists between affluent nations, such as G20 members, and others in developing this infrastructure. While wealthier nations utilize advanced spatial modelling, others often rely on ad-hoc methods lacking quantitative rigor. This paper proposes a spatial modelling approach using open-source and private data tailored for fleet operators. Our method optimally identifies locations and capacities for new urban charging stations via a novel software pipeline. Fleet operators can leverage this to select warehouse locations, enhance infrastructure, and perform scenario analyses in an agent-based model. A case study in Mumbai and Delhi demonstrates substantial improvements: a 70% reduction in charger waiting times, a 20% decrease in time to reach a charger, and a 70% reduction in charging duration, among other key performance indicators.