Some uncertainties such as the stochastic input/output power of a plug-in electric vehicle due to its stochastic charging and discharging schedule， that of a wind unit and that of a photovoltaic generation source， volatile fuel prices and future uncertain load growth， all together could lead to some risks in determining the optimal siting and sizing of distributed generators（DGs） in distribution systems. Given this background， under the chance constrained programming（CCP） framework， a new method is presented to handle these uncertainties in the optimal siting and sizing problem of DGs. First， a mathematical model of CCP is developed with the minimization of DGs investment cost， operation cost and maintenance cost as well as the network loss cost as the objective， security limitations as constraints， the siting and sizing of DGs as optimization variables. Then， a Monte Carlo simulation embedded genetic algorithm approach is developed to solve the developed CCP model. Finally， the IEEE 37-node test feeder is employed to verify the feasibility and effectiveness of the developed model and method.