Representing the first-generation high-performance water-based drilling fluid, KCl/Polymer drilling fluidsare widely used to drill troublesome shale formations containing water-sensitive clay minerals. In additionto maintaining wellbore stability, its rheological properties also play a crucial role in enhancing overalldrilling performance. An accurate description of the rheological behavior of drilling fluids is essential inoptimizing drilling fluid hydraulics. This study evaluates traditional and novel optimization algorithmsfor the parameterization of rheological models using an extensive field rheological database of KCl/Polymer drilling fluids. An objective function based on a symmetric mean absolute percent error is used inparameterizing rheological models. Golden Section Search (GSS), Generalized Reduced Gradient (GRG),and Trust Region (TR) methods are used as new alternatives to traditional Gaussian-Newton (GN) andlinear/semi-linear regression (LR/QLR) methods. As a more statistically plausible criterion, the symmetricmean absolute percentage error is also used to measure the goodness of fit of rheological models withdatasets. It has been shown that GRG and TR algorithms outperform conventional methods in findingoptimal model parameters. The three- and four-parameter models fitted the rheological data best, with amore uniform symmetrical error distribution than the two-parameter models.