The Voltage Response measurement since its introduction in the 1960s has been used successfully for the diagnostics of electrical insulation. The method is based on two quantities of decay and return voltage slopes and can be used to study the conduction and polarization processes inside the insulation. Extended Voltage Response method, being an advanced version of the Voltage Response measurement helps in further studying the polarization process by using a large polarization spectrum and hence dielectric relaxation processes. These dielectric relaxation processes can be modeled by the Debye model. Since as most of the techniques used for diagnostic purpose does not give the information about the conduction and polarization processes separately, it is difficult to determine the R-C parameters of the Debye model. The Voltage Response technique is very useful in this regard because of the two voltage slopes. The paper shows a novel experimental benchmark for testing the function fitting methodologies of the Voltage Response methodologies, which helps in determining the R-C parameters. Moreover, the problem can be used for testing the novel genetic, evolutionary algorithms, where benchmarking is an actual challenge. The proposed nonlinear function fitting method uses the genetic algorithms via the Ārtap framework, which lets it possible to select the most accurate optimization algorithm from the provided list of the algorithms and achieve better fitting precision, faster calculation time or more powerful processing ability.