A multi-objective planning scheme based on chance-constrained programming and Monte-Carlo method for wind generations and photovoltaic allocating is proposed to deal with the randomness and relevance of wind speed, solar radiation, and load. In the mathematic model, three indexes are introduced to evluate distributed generation profits, namely cost index, power loss index and voltage deviation index. Monte-Carlo method costs large computation time, therefore a method of established probability distribution based on multi-interval division is introduced to reduce the number of sampling. In the process of solving the model, the first multiobjective differential evolution is employed to get a set of pareto optimal solutions, then fuzzy multi-attribute decision making method based on information entropy is adopted to select the best compromise solution from the pareto optimal solutions. The case studies are carried out on the IEEE-33 nodes distribution network, and the results show that the prosed optimal model is rational, and the algorithm is effective.