The clustering problem, or grouping, has two variants. If the number of clusters is predefined, this problem is known as the Clustering Problem (CP) or k-Clustering Problem, but when the number of clusters is not defined, the problem is known as the Automatic Clustering Problem (ACP). This paper proposes a new multi-population Biased Random-Key Genetic Algorithm (BRKGA) for the ACP, considering the silhouette index as similarity measure. In algorithm, several BRKGA populations evolve independently, such that each population is responsible for searching the best clustering for a given cluster number, i.e., each population solves one k-Clustering Problem for a particular k. Extensive experiments in 53 benchmark instances commonly used in the literature show that the algorithm obtained very competitive results compared to the state-of-the-art algorithms.