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Renewable energy resources and transport electrification have become essential components of modern and future distribution planning. Despite the impetus of economic and environmental benefits, the uncertainty brought in poses challenges. In this paper, we propose an integrated expansion planning framework based on a multiobjective mixed-integer nonlinear program. The aim is to minimize the net present value of investments considering feeder routing, substation alterations and construction while maximizing the utilization of proposed charging stations. Distributed generation and load uncertainties, recast in two-stage stochastic programming, are tackled with a scenario generation and reduction technique using a probabilistic approach with Kantorovich metrics. The final number of the scenarios are validated with an alternative clustering method. A flow-based location–allocation theory with user equilibrium traffic assignment model is exploited to site charging stations. The sizing problem is determined using continuous-time Markov chain modeling. The proposed framework is solved with a multiobjective Tchebycheff decomposition-based evolutionary algorithm and tested on a modified 54 bus distribution network and 25 transportation node system. Numerical results demonstrate the capability of the proposed method. Distribution planning authorities can benefit from the presented approach to make intertemporal investment decisions while maintaining the quality of system performance. • Optimal Integrated distribution plan coupled with road electricity is identified. • Uncertainty is handled using scenario generation and reduction approach. • The final branches of scenario tree are verified by k-means clustering method. • Stochastic modeling with Markov Chain solves charging station planning problem. [ABSTRACT FROM AUTHOR]