This presentation explores innovative methods for the optimization and metamodeling of complex functions defined over sets of vectors (or "clouds of points"), with various applications such as wind-farm layout or experimental design optimization. Unlike more common functions defined over vectors, functions defined over sets vectors have the specificity of being invariant with respect to the vectors permutations. Additionally, the size of the sets varies. Finally, in this work, the functions are considered as "black-boxes", meaning that no information regarding their regularity and derivates is known. The first part of this work addresses the optimization of such functions using evolutionary algorithms, focusing on stochastic perturbation operators, such as crossovers and mutations, which are developed using the Wasserstein metric. By representing sets of points as discrete measures, we leverage the Wasserstein barycenter for designing these evolutionary operators. This approach enables a precise representation of the functions’ geometric information, balancing contraction and expansion effects to enhance optimization. In order to handle cases in which the functions are computationally costly, the metamodeling of functions defined over sets of points is also studied, with a focus on Gaussian processes. We employ substitution kernels based on Euclidean, sliced-Wasserstein, and Maximum Mean Discrepancy (MMD) distances to model complex inter-point relationships, with MMD kernels yielding particularly promising results in wind-farm simulations by adapting to the predominant wind direction. The presentation concludes by discussing cases where the vectors belong to a non-convex domain. We also briefly show how all this work can come together to carry out Bayesian optimization over sets of vectors.