Electric vehicles (EVs) are significantly increasing the burden on the electricity sector, resulting in power loss, a poor voltage profile, and voltage stability difficulties. To address this, a solid planning framework for efficient deployment of EV charging stations and solar energy resources in the distribution network is being established in the present work. A practical probabilistic model of EV is developed using Probability Distribution Function (PDF) considering uncertainty parameters i.e.trip distance, trip end time, types of battery and battery capacity extracted from real National Household Travel Survey (NHTS) datasheet. The Teaching Learning Based Optimization (TLBO) algorithm is used to solve the problem of optimum joint deployment of EV charging station and solar energy, minimizing power loss reduction index (PLRI) and voltage deviation index(VDI) while considering system constraints. Further the voltage stability of the system is also analyzed with the incorporation of devices in the network. In this paper, the optimization problem is solved using MATLAB (version R2018a) software, and the IEEE 69 bus system is employed as a test system. The simulation outcomes show that optimal deployment of EV charging stations with photovoltaic (PV) units improves voltage profile, the penetration level of EV charging station and also voltage stability index (VSI) of the system, but reduces total real loss. The technique is compared with three different algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), in terms of convergence characteristics.