solution optimization in various industries. [PERSON] et al. [2] combined the small population real-coded genetic algorithm with the BP neural network to optimize the design of the voltage sharing circuit of the high-voltage DC voltage divider. [PERSON] et al. [3] established a 0-1 linear programming model for optimal water distribution in branch and lateral canals in irrigation districts, and analyzes the advantages and disadvantages of using discrete binary particle swarm algorithm (BPSO) and genetic algorithm (GA) to optimize water distribution in irrigation districts. [PERSON] et al. [4] used a double-objective genetic algorithm based on discrete variables to optimize the cross-section design of concrete frame columns, using carbon emissions and cost as indicators. Some scholars have also combined artificial neural networks with genetic algorithms for scheme optimization and prediction. [PERSON] et al. [5; 6] predicted the martensite fraction of microalloyed steel and simulated the correlation between heat treatment, chemical composition, and bainite fraction of pipeline steel by means of artificial neural networks. [PERSON] et al. [7] established a neural network model for load prediction based on a 4 million tons per year Fischer-Tropsch synthesis process. The genetic algorithm was used to optimize the initial weights and thresholds of the neural network to improve the accuracy of model prediction. Applying the GA to cost optimization can effectively improve the optimization efficiency and accuracy [8; 9; 10]. [PERSON] et al. [11; 12] studied the cost optimization problem under the condition of minimizing the cost of project group owners using real number encoding and a genetic algorithm that preserves elite offspring. [PERSON] et al. [13] considered the time value of capital, established a construction time-cost dynamic optimization model, and solved it through genetic algorithm. [PERSON] et al. [14] established a mathematical model for dynamic time/cost optimization and proposed an improved incentive genetic algorithm. [PERSON] et al. [15] overcame the shortcomings of traditional network planning optimization using genetic algorithms, making cost optimization more accurate and able to be solved more efficiently. The construction time-cost optimization based on genetic algorithms requires a large amount of engineering data, with the expansion of modern building scale and the increase in construction process complexity, the limitations of traditional optimization methods in obtaining engineering data are becoming increasingly prominent. Building Information Modeling (BIM), as an emerging information technology, is gradually being applied to engineering construction optimization [16; 17; 18]. [PERSON] et al. [19] constructed a schedule-cost optimization system for grid construction projects based on BIM and the genetic algorithm, fully leveraging the advantages of modern information technology and traditional theoretical methods. [PERSON] et al. [20] studied the combination of NSGA--II algorithm and BIM5D to solve the construction time-cost optimization problem. BIM technology can accurately and quickly obtain project parameters and resource information, solving problems such as complex super and deep excavation processes and large engineering data statistics; the GA can efficiently search for the optimal solution within the global domain, improving optimization accuracy, combining BIM technology with GA for construction time-cost optimization, which can fully leverage the advantages of both. In summary, the genetic algorithm has been widely applied in scheme optimization in various industries, and scholars have conducted extensive research and exploration. Some scholars have also combined BIM technology with genetic algorithms for time-cost optimization, but few have considered considering the time value of capital and the rewards and punishments of project duration.