This paper explores the application and analysis of intelligent optimization algorithms in the monitoring and control methods for civil engineering structures. As the scale and complexity of civil engineering projects continue to expand, traditional methods for structural deformation monitoring and control have become inadequate to meet practical needs. Intelligent optimization algorithms, with their strong adaptability, robustness, and efficiency, offer new approaches and methods to address these challenges. The study first introduces several typical intelligent optimization algorithms, including genetic algorithms, particle swarm optimization, and ant colony algorithms, analyzing their principles and characteristics in the monitoring and control of structural deformations. It then focuses on specific applications of these algorithms in sensor placement optimization, data processing, model identification, and control strategy optimization. Through case studies, the advantages of intelligent optimization algorithms over traditional methods in terms of monitoring accuracy, computational efficiency, and adaptability are compared. The research findings indicate that intelligent optimization algorithms can significantly enhance the accuracy and reliability of structural deformation monitoring in civil engineering, while providing more scientific and efficient solutions for structural deformation control. Finally, the paper discusses the development trends and challenges faced by intelligent optimization algorithms in this field, offering directions for future research.