Construction projects are characterized by their large scale, complex processes, involvement of multiple disciplines, substantial capital investment, and extended construction periods. Any delays or rework in the construction process can result in incalculable losses. To address these challenges, this paper ?rst establishes a component coding system and expands the corresponding attribute information in the BIM model based on the attribute requirements of each coding level. Subsequently, a multi-objective optimization mathematical model is developed, incorporating time, cost, quality, and safety objective functions. Using a genetic algorithm for population iteration and selection, the results are used as the initial population for further optimization via the ant colony algorithm. Experimental results indicate that the proposed method enhances algorithm ef?ciency, prevents premature convergence or local optima, and achieves convergence after 40 iterations. Moreover, the method extends actual construction progress information within the model, reducing the workload associated with traditional scheduling. The study concludes by presenting an intelligent optimization model for construction projects, offering new perspectives and technical methods for the development of the construction industry.