Urban buildings are places that effectively promote the conservation and rational use of energy resources, which are conducive to accelerate the development of a circular low-carbon economy. A multi-objective optimization model for intelligent building load optimization from two aspects of economy and acceptability is proposed. Firstly, time/load-related indicators are established to describe the users′ acceptance of participating in load regulation for the transferable and reducible load. Then, with the goal of minimizing the peak-to-valley load difference in the statistical period, achieving the highest acceptability, and minimizing electricity costs, the building load combined mathematical model is established. Finally, it is proposed to solve the model based on continuous Hopfield neural network（CHNN）. The analysis of the calculation example shows that the multi-objective optimization model can effectively reduce the electricity cost and ensure the acceptability of electricity.