Induction Motors (IM) are the most used electrical machines in the industry, where they use significant energy percentages. The control of IM requires the knowledge of their behavior; in this sense, it is necessary to accurately estimate the internal parameters that control their performance. This process involves the optimization of linear models with different constraints. Evolutionary Algorithms (EA) are proven techniques designed to obtain better results than classical optimization methods. Most of EA suffer some limitations such as slow convergence; they also have a large number of parameters that need to be set by the designer. Therefore, to solve this problem, this paper proposes an alternative method to estimate the parameters of IM using the Electromagnetism-Like Optimization (EMO) algorithm. EMO has the advantage of using a small number of iterations. The experimental results and comparisons show that the proposed approach gives better results than related methods, regarding accuracy and convergence.