Structural integrity assessment plays a crucial role in ensuring the safety and functionality of engineering systems. Cracks can lead to severe performance degradation, making their detection and characterization essential for effective maintenance and damage prevention. To enhance the precision of detection, relative frequency shifts (RFSs) are employed as a crack signature, providing a quantifiable and reliable measure of structural damage. This study presents a comparative analysis of three optimization techniques - Differential Evolution, Bayesian Optimization, and Simulated Annealing - applied to structural crack assessment. These techniques aim to identify the best match between the RFSs obtained from measurements and the computed RFSs, which represent a comprehensive range of possible damage scenarios. The effectiveness of each method is evaluated based on multiple performance criteria, such as computational efficiency and robustness in identifying crack locations and characteristics. Through extensive numerical simulations and performance benchmarking, key insights are drawn regarding the advantages and limitations of each optimization technique. The findings serve as a valuable resource for selecting appropriate methods tailored to specific structural health monitoring applications.