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A powerful blend based on a three-phase distribution power flow method and a Genetic Algorithm for plug-in electric vehicular fleets scheduling is proposed in this paper. The Genetic Algorithm optimizes the number of charging and discharging plug-in electric vehicles in order to efficiently manage voltage unbalances and power losses. The plug-in electric vehicle based on a voltage controlled representation is incorporated into a power flow formulation suitable for radial and unbalance distribution network. The PEV model comprises a voltage source converted (VSC) and a battery pack. While active power is regulated at the storage device according to the charging and discharging status of battery, the voltage magnitude at the point of common coupling is regulated by the VSC. Furthermore, a comprehensive VSC-based PEV equivalent model that accurately reflects the behavior of a distributed vehicular fleet is proposed in this work to carry-out efficient steady-state analyses. The impact of a plug-in vehicular fleet in the voltage unbalance of the IEEE 13-node test feeder is optimized with a multiobjective genetic algorithm, where each PEV is modeled as a Tesla Roadster EV with a lithium-ion battery pack.