The characteristics of electric power generation, transmission and distribution in the USA are such that experts have clearly identified local distribution as the most likely component of the chain to be adversely affected by unregulated Plug-in Hybrid Electric Vehicle (PHEV) charging. This paper presents a study performed to find the impact of PHEV charging on the local distribution transformer. Monte Carlo based mass simulation of highly clustered PHEVs was conducted based on statistical representations of some key factors like vehicle characteristics and vehicle usage patterns. The presented methodology may assist in determining the most suitable local/regional charging strategies for PHEVs. In our earlier paper (Gong et al., 2010), some general and basic analysis was carried out for a PHEV fleet. A systematic statistic analysis using clustering approach was performed for the real world velocity profiles. And Markov-chain model was used for velocity profile generation for different driving patterns. The work in this paper is based on this earlier work, since for mass simulation we need to estimation the driving patterns for various drivers. Monte Carlo simulation was used for mass simulation of PHEV energy analysis with uncertain inputs of vehicle characteristics, initial conditions and usage patterns. The PHEV energy analysis model was developed to predict energy consumption in a statistic way. Based on the stochastic input models developed from real driving data (or data from public resources), the stochastic output can be obtained. Driving pattern recognition was used in the model to estimate the fuel economy for charging sustaining mode driving instead of using complicated and time consuming simulation models. A PHEV fleet consisting of 500 PHEVs was studied in this work, and the output results gave an effective estimation of the electric energy consumption, final SOC (State of Charge) estimation, fuel economy estimation etc. The model described in this paper presents a useful tool that can be used in many applications.