Shape analysis is a key task in computer vision, and multiscale descriptors can significantly enhance shape characterization. However, these descriptors often rely on parameter adjustments to configure a meaningful set of scales that can enable shape analysis. Parameter adjustment in large image datasets is often done on a trial-and-error basis, and an alternative solution to mitigate such a limitation is the use of metaheuristic optimization. The main contribution of this paper is to provide a strategy that supports the automatic parameter adjustment of a multiscale descriptor within a metaheuristic optimization algorithm, where the choice of the cost function strongly influences and boosts the performance of the shape description, which is closely related to the problem domain, i.e. the image dataset. Our research considers synthetic data in a prior evaluation of the cost functions that optimize the scale parameters of the Normalized Multiscale Bending Energy (NMBE) descriptor through the Simulated Annealing (SA) metaheuristic. The cost functions that drive this metaheuristic are: Silhouette (SI), the Davies–Bouldin index (DB) and the Calinski-Harabasz index (CH). We conduct content-based image retrieval and classification experiments to assess the optimized descriptor using three healthcare image datasets: Amphetamine Type Stimulants (ATS) pills (Illicit Pills), pills from the National Library of Medicine (NLM Pills) and hand alphabet gestures (Hands). We also provide segmentation masks for Illicit Pills to guarantee reproducibility. We report the results of tests using a state-of-art method based on a deep neural network, Inception-ResNet-v2. The optimized NMBE with SI and DB achieved competitive and accurate values of above 94%, in terms of both the Mean Average Precision measure (MAP) and Accuracy (ACC) for Illicit Pills and NLM Pills. The precision recall curves demonstrate that it outperforms the Inception-ResNet-v2 for both of these datasets.