Metaheuristic optimization schemes have recently become a topic for the development of diverse searching mechanisms proposed for solving a wide range of problems with different fields of application. For metaheuristic algorithms, configuring the initial set of candidate solutions is crucial for the emergence of efficient searching strategies capable of finding the global solution with minor resources. Despite the numerous advantages that a proper initialization may bring to the optimization process, most researchers tend to focus efforts on developing evolutionary operators, despising the different advantages of creating diverse configurations. Most metaheuristic algorithms reported in the literature employ randomly generated configurations for their population, employing easy generation of values. Nevertheless, random numbers present issues as the grouping of solutions in determined regions, an issue commonly present in high-dimensional landscapes. This process generates search patterns and obtains feasible solutions, getting different configurations considering the spatial distribution, as the performance is obtained by the solutions in this first stage. This chapter is intended to present a novel configuration for initializing populations in metaheuristic algorithms based on the Gibbs sampling methodology. This methodology employs a set of calculations of samples determined for each decision variable of a particle in the population through a conditional distribution considering a previously generated sampling. Generating different samples generates populations with a minor degree of correlation between the individuals, generating a better spatial distribution even in high-dimensional scenarios.