We propose an unconstrained global continuous optimization method based on tabu search and harmony search to support the design of fuzzy linear regression (FLR) models. Tabu and harmony search strategies are used for diversification and intensification of FLR, respectively. The proposed approach offers the flexibility to use any kind of an objective function based on client's requirements or requests and the nature of the dataset and then attains its minimum error. Moreover, we elaborate on the error produced by this method and compare it with the errors resulting from the other known estimation methods. To study the performance of the method, three categories of datasets are considered: Numeric inputs-symmetric fuzzy outputs, symmetric fuzzy inputs-symmetric fuzzy outputs, and numeric inputs-asymmetric fuzzy outputs. Through a series of experiments, we demonstrate that in terms of the produced error with different model-fitting measurements, the proposed method outperforms or is Pareto-equivalent to the existing methods reported in the literature.