In order to reduce the influence of wind uncertainty on integrated energy system, day-ahead and intra-day optimal scheduling is proposed. Firstly, based on Monte Carlo sampling method, day-ahead and intra-day optimal scheduling scenarios of photovoltaic and wind power are generated and cut. Then, the day-ahead optimization scheduling takes the minimum operating cost as the objective function, introduces time-of-use price and time-of-use heat price, constructs four scenarios, and analyzes the operation cost and energy consumption of the four scenarios. Then, a multi-objective optimization model is constructed to minimize the operation cost and scheduling adjustment cost. Finally, the proposed model is solved by cplex solver in Matlab to verify the accuracy of the proposed model and the effectiveness of the method. The results show that the proposed strategy can effectively improve the consumption of renewable energy and reduce the system operation cost.