The aim of this paper is to investigate the electric vehicle (EV) user’s decision-making process in the province of Saskatchewan, Canada in which they decide whether to charge their EV after a trip or not based on preference data. The collected data provides charging and driving characteristics of individual users, in a sample span of a month. A discrete choice analysis is performed to identify the relevant variables in the decision-making process. Both a logit and probit models were developed to estimate the degree to which specific variables affect users’ behaviour. The explanatory variables were carefully selected to avoid collinearity by performing Pearson Correlation Coefficient and Variable Inflation Factor Analysis. Both models (logit and probit) yield similar results, the probit model was ultimately selected based on lower Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A dummy variable was included to differentiate between Tesla users named “Supercharging”, who currently hold the biggest share in the EV market, as well as supercharging infrastructure access. The implemented model suggests that the trip’s final state of charge percentage and the following trip distance travelled in kilometers are the two main contributors for whether the EV user decides to charge their vehicle after a trip, followed by “Nighttime” charging. In contrast, having access to supercharging infrastructure does not show any significant impact on the charging behaviour. These findings could potentially be used for the optimization of the province’s EV charging infrastructure as well as the electric power supply network for the widest adoption of EVs.