Evolutionary computation has been successfully applied to building energy conservation design problems because of excellent global search capabilities. However, since the need to continuously evaluate individuals by expensive building energy consumption software, existing evolutionary algorithms generally suffer from high operating cost. In view of this, this paper proposes a multi-surrogate assisted multi-objective evolutionary algorithm for building energy conservation design, called MS-MOEA/D. Firstly, multiple basic surrogate models are constructed simultaneously according to the objective decomposition characteristic of the multi-objective evolutionary algorithm MOEA/D. Then, for each individual that needs to be evaluated, the appropriate base surrogate model is automatically selected, and their integration results are used to predict the objective value of the individual, so as to improve its prediction accuracy. At the same time, the update timing and the scale of the basic surrogate model are determined autonomously in the evolution process, in order to reduce the management cost of the surrogate model. The MS-MOEA/D is integrated with the software EnergyPlus to establish a multi-objective evolutionary simulation platform for building energy conservation design, and the platform is used in the energy conservation design examples of atypical residential and office buildings in Beijing, China. Comparing with seven classical multi-objective evolutionary algorithms, experimental results show that the MS-MOEA/D can obtain a highly competitive Pareto optimal solution set while significantly reducing the computational cost.