This study proposes an infrared defect detection and evaluation framework for hypervelocity impacts based on virtual objective vectors. The framework integrates multi-objective optimization with infrared feature extraction, employing a clustering multi-objective optimization approach to extract infrared features for different defect categories. Dynamic time warping is used to extract typical transient thermal responses, while virtual objective vectors are applied to extend the weight vector set and enhance the estimation accuracy of the Pareto front. Experimental results demonstrate that this method effectively improves infrared defect detection accuracy under hypervelocity impacts, providing a reliable basis for spacecraft damage assessment.