Unstructured, dynamic bioreactor models of complex processes usually possess many nonidentifiable, insensitive, or statistically nonsignificant parameters. However, an exhaustive search to find a reduced set of identifiable parameters is computationally demanding. We developed a heuristic iterative procedure for parameter optimization (HIPPO), generic and free of symbolic mathematical manipulations, to help bioprocess engineers to find and estimate a set of significant parameters, thus obtaining reliable models. This methodology includes local sensitivity, significance and identifiability analysis, metaheuristic optimization, and fitting performance assessments. We tested this method with two bioreactor models: a microalgal fed-batch bioreactor (MFB) 12-parameter model and a solid substrate fermentation (SSF) 14-parameter model. Applying this procedure, we were able to find a MFB reparametrization with 4 fitting parameters after 479 iterations and a SSF reparametrization with 6 fitting parameters after 918 iterations. In both cases, all fitting parameters were statistically significant and accurately estimated and models had a better fit than the originally fitted 12- and 14-parameter models. The heuristic was programmed using MATLAB and is freely available to the research community (http: //www.systemsbiology.cl/tools/)