Abstract: Wind power output is very unstable and stochasticity and variablility, making it difficult to control accurately in integrated electric and heating systems. We propose a multi-objective and two-stage stochastic programming method (MOTSP) to minimize cost and wind curtailment. First, the cost minimization part is modeled and analyzed by a two-stage stochastic programming model. The first stage considered the start-up and shut-down costs of the thermal power unit as its objective, while the second stage considered the unit's operating cost as its objective. Finally, the multi-objective algorithm is used to solve the problem. This method uses Gaussian distribution to describe the load and wind power forecast errors to solve the uncertainty of the wind power output, the Monte Carlo method to generate random scenes， and the back reduction method to reduce the scenes. The simulation results showed that the MOTSP algorithm proposed in this study is more uniform and generalizable compared to other intelligent algorithms and has better convergence， which can minimize wind abandonment and unit operating costs.