The study [16] is related to the Italian province of Florence and uses GPS real data from around 12,000 ICE vehicles, extracting driving and parking patterns to size the charging infrastructure; anyway, it does not explicitly give as an output a number of CS and the relative cost, and it does not consider a temporal evolution of the analyzed situation. Lastly, [17; 18] give as an output the forecast size of the charging infrastructure for two different Swiss municipalities, but neither of them give a proper explanation on the methodology used. Scope of this work is to define the optimal size of a minimum cost charging infrastructure, suitable for deployment in an urban area and able to cope with the requests of a given passenger car EV fleet. This study focuses only on EV passenger cars since they account for the vast majority of EU-28 circulating road vehicles with an 87% share of total, while, in comparison, light commercial vehicles accounted for 9.8% (authors elaboration on 2016 Eurostat data [19]). Moreover, this paper will investigate the impact of such infrastructure on the existing parking stalls and on the electrical network, in terms of the average energy and power requests; finally, it will characterize the infrastructure using performance indicators (PI) such as average daily charging events and global utilization rate. In order to address these requests, an analytical model has been developed, separately accounting for battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) fleets, as well as for publicly accessible and private residential CS. Given the primary importance of the inputs related to the size of the circulating EV fleet, an extensive literature research for EV fleet growth forecasts has been carried out, focusing on reports related to the two considered areas as well as to the analyzed timeframe. Since substantial differences between the various forecasts emerged from the research, a novel method for data sorting and conditioning has been developed, using circulating fleet turnover rate as threshold indicator. The model has then been applied to the two geographical areas of Florence Municipality for Italy and Bruxelles for Belgium, over the 2020-2030 period, with a 1-year timestep resolution. Within each area nine scenarios have been evaluated; they are obtained as the combination of three different EV circulating fleet forecasts with three possible user's charging behaviors. The results from this study could be used to provide further insights to policy makers and local authorities, in order to better understand and manage the transition period toward a higher share of urban electrical mobility. To the best of the authors' knowledge, within the available studies, the one which shares the closest similarities with present work's purposes is [20]; anyway, it describes the charging infrastructure with a lower level of details and it does not implement a model for the estimating of EV fleet growth, using as input data coming from a single source. This research article is structured as follows: in Section 2, the inputs needed by the model together with the related procedures are first defined, whenever relevant; then, the model structure and embedded algorithms are thoroughly presented. Finally, the outputs and the rationale for the input scenarios implemented in this paper are reported. The first part of Section 3 reports details of the numerical values of the inputs used in the various scenario, while the second part discusses the obtained results, using also several PI. Finally, summary conclusions and closing remarks are given in Section 4.