Non-destructive testing methods are essential in civil engineering applications, such as evaluating the compressive strength of masonry. This paper presents a fuzzy signature model based on non-destructive in situ measurements and visual inspection, applying weighted geometric mean aggregation in the signature vertices determined by experts. The weights of the aggregation terms were optimized using the Monte Carlo method, genetic algorithm and particle swarm algorithm to ensure that the evaluation by the signature aligned with the results of destructive tests performed on existing masonry. The results of the methods were compared for single and multiple assembled masonry structures using the same objective function. All three methods provided relatively high confidence in finding the extreme values of the objective function on a generated dataset, which accounted for the correlations observed in actual measurements. Accordingly, validation based on real data yielded the expected results, thus demonstrating the model’s suitability for practical application. This study assessed the inherent, analyzing whether symmetric or asymmetric weight distributions affected evaluation consistency. While symmetric weighting simplified aggregation, asymmetry allowed local structural irregularities to be highlighted. In addition, the cost analysis of the optimization methods revealed a disparity in computational cost increments between the two approaches. The presented work outlines the advantages of the different methods and their applicability to structural assessment.