Currently, economic information systems are mainly responsible for processing, storing, and transmitting a large amount of economic data, and their reliability is crucial for ensuring the normal operation of organizations. However, traditional optimization methods are difficult to cope with the increasing system complexity and constantly changing needs, so this article introduces genetic algorithms for optimization and improvement. This article focuses on using genetic algorithms to conduct a detailed exploration of system parameter tuning and configuration selection, as well as multi-objective optimization and decision support. Through two sets of simulation experiments, the following conclusions are drawn: compared with traditional greedy algorithms, the overall average improvement in stability indicators is 6.65%, while the overall average reduction in response time indicators is 55.7 ms. This study not only expands the understanding of reliability optimization of economic information systems in theory, but also provides an effective tool for enterprises in practical applications. Through the application of genetic algorithms, comprehensive optimization of system performance has been achieved, reducing fault risk, improving stability and efficiency for enterprises, and providing substantial improvement strategies for the reliable operation of economic information systems in complex economic environments.