Currently, major enterprise groups are using financial management systems as an administrative affairs management platform, which is mainly used to handle daily financial accounting work and adopts a centralized financial data model. With the frequent use of the system, a large amount of business data is generated in the provincial bureau database every day, and these historical data are usually only used as query summaries. The data utilization rate is not high, wasting a lot of resources, and more importantly, it cannot provide effective decision-making support to the leadership. This article proposed an analysis method for optimizing financial management based on genetic algorithm (GA), and provided a detailed introduction to the implementation process of this method. The study transformed information into decision-supportive information for leaders through an intelligent data processing process. For this research direction, the paper used GA to construct a basic classifier and performs classification prediction and accuracy verification on public datasets and financial expenditure datasets on an experimental platform. Finally, the study found that from the perspective of average response time, it was optimal for the system to process financial data within 100, and the average time to process these data was within 12 s. This result is superior to other financial management systems in terms of processing time and can be put into practical application, providing new methods for modern enterprises to improve management efficiency.