With considerations of the uncertainties on wind power/load prediction errors and random outages of generators, a day-ahead assessing model is presented to assess wind power integration capability on the basis of operation senario optimaztions. The model presented here is a typical multi-objectively optimization formulation, and its two optimization objects, i.e., wind power integating capacties maximization and generating costs minimzation are inherently conflicting each other and can not get their optimal results simultaneously. Non-dominated sorting genetic algorithm (NSGA) is utilized to obtain Pareto optimal result set of the formulation, from which, day-ahead minimum and maximum wind power injection amounts can be obtained. On the basis of Pareto optimal result set obtained by NSGA, average generation costs and average wind power integration costs with respect to different wind power injection amounts are calculated and analyzed. Simulation results on IEEE 118-node systems justified the formulation and solving technique presented in this paper. Wind power injection amounts and their costs can provide supports for day-ahead dispatching decision.