Abstract: The layouts of sensors located both inside and outside the small satellite structure during dynamic test directly affect the test efficiency and period. After fully considering the correlation between internal and external measurement points, an intelligent algorithm-based sensor layout optimization method was proposed. It was intended to reduce the number of internal measurement points while ensuring the independence of external ones. Initially, a frequency response function matrix of internal and external measurement points was constructed through frequency-sweeping analysis of the satellite structure. Subsequently, the external measurement points were categorized into different groups using principal component analysis and K-means clustering analysis. Based on this, the optimization objective was determined to maximize the correlation coefficient of frequency response functions between internal and external measurement points. Genetic algorithms were utilized to select appropriate measurement points from the obtained categories to optimize the sensor layout. Furthermore, a modified information entropy approach was applied to optimize the number of internal measurement sensors in the satellite structure. Through practical applications in a specific small satellite, the proposed method was verified to be able to achieve a good number reduction in internal sensors.