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As in other complex signal processing tasks, ECG beat delineation algorithms are usually constituted of a set of processing modules, each one characterized by a certain number of parameters (filter cutoff frequencies, threshold levels, time windows, etc.). It is well recognized that the adjustment of these parameters is a complex task that is traditionally performed empirically and manually, based on the experience of the designer. In this paper, we propose a new automated and quantitative method to optimize the parameters of such complex signal processing algorithms. To solve this multiobjective optimization problem, an evolutionary algorithm (EA) is proposed. This method for parameter optimization is applied to a wavelet-transform-based ECG delineator that has previously shown interesting performance. An evaluation of the final delineator, using the optimal parameters, has been performed on the QT database from Physionet and results are compared with previous algorithms reported in the literature. The optimized parameters provide a more accurate delineation, with a global improvement of 7.7%, over all the criteria evaluated, and over the best results found in the literature, which is a proof of interest in the approach.