The application of visualization technology in water conservancy projects is in the ascendant. It is necessary to analyze and study the key technologies and difficulties involved in 3D visualization simulation of water conservancy projects in combination with relevant research work, and provide corresponding solutions. Most water conservancy projects are large and complex in recent years, the rapid economic development has led to increased energy consumption, and the demand for the development of clean and renewable resources such as water is growing. 3D visual engineering design has entered the practical stage in the advanced industry, but its development in the hydraulic field lags behind. In order to solve the problem that traditional genetic algorithms are easy to fall into local optimal solutions and have unstable performance, an adaptive genetic algorithm (AGA) based on co evolution is proposed. On the basis of the two-layer framework model of co evolution, an adaptive mutation strategy is introduced to improve the local evolution operation in the co evolution genetic algorithm and strengthen the local search in the upper layer. That is, the global mutation operator and the local mutation operator work together to improve the distribution characteristics of the population, strengthen the global convergence ability of the algorithm. The genetic algorithm of the probability model is used to optimize four famous test functions. The experimental results show that the algorithm is not easy to fall into the local extreme value. The convergence speed is fast and the implementation is simple.