Clustering is a popular data analysis technique to identify homogeneous groups of objects based on the values of their attributes, used in many disciplines and applications. This extended abstract of our undergraduate thesis for obtaining the engineer degree in informatics and systems, presents an approach based on the Social Spider Optimization (SSO) algorithm for optimizing clusters of data, taking as metric the sum of euclidean distances. Other important algorithms of the literature were implemented in order to make comparisons: K-means algorithm, and a Genetic Algorithm (GA) for Clustering. Experiments were performed using 5 datasets taken from the UCI Machine Learning Repository, each algorithm was executed many times and then the following measures were calculated: mean, median, minimum, and maximum values of the results. These experiments showed that the SSO algorithm outperforms the K- means algorithm, and it has results equally competitive as the GA. All these results were confirmed by statistical tests performed over the outputs of the algorithm.