In recent years, most of the new buildings in China are high-energy buildings, and electromechanical system, as a construction project, occupies a large proportion of energy consumption, which is an important way to improve the green design of buildings. The components of electromechanical system are complex, which have dialectical relationship between performance indexes under the multi-objective optimization of low energy consumption and low cost. Aiming at the common problem of high energy consumption in China's new buildings, this paper comprehensively applied NSGA-II method in genetic algorithm and building information technology (BIM), combined with Matlab, Revit, Ecotect and other computing tools. Seven indexes of electromechanical energy consumption have been analyzed for multi-objective optimization design, which namely ACE, REE, CE, HE, LE, BMS and EE The case study of Shandong Province hospital project is selected to reveal the high share of building building electromechanical system in energy consumption through the optimised energy saving effect, and at the same time, there is a huge space for energy saving optimisation. On the basis of BIM technology, genetic algorithm is used to find the optimal solution set of multi-factors of building electromechanical system, so as to provide data support for solving multi-objective and multi-variable problems in green building design. The main conclusions are as follows: (1) The NSGA-II method is suitable for the energy consumption optimization calculation of electromechanical equipment in green building projects, and the efficiency and quality of index optimization analysis can be improved by quickly searching Pareto optimal solution set through multiple iterations, which provides an important reference for scheme optimization in the design stage; (2) Pareto optimal solution sets of seven indexes of electromechanical energy consumption are obtained. The highest variation rates are CE(3.23%) and HE(2.67%), and the lowest variation rate is BMS(0.16%). (3) Through optimization simulation calculation, it is found that ACE, REE, HE and LE have a large contribution to reducing the overall energy consumption, while CE, BMS and EE have certain fluctuations before and after optimization.