One of the most significant error contributors to preliminary design tools for Photovoltaic power systems is related to the simple parametric Clear Sky models. Therefore, this paper focuses on providing a methodology and a more sophisticated open-source tool for 3 commonly used Clear Sky models. This includes all relevant steps involved in the process - from filtering the raw meteorological data, identification of Clear Sky regions, data redistribution to genetic optimization of selected model parameter, etc.use case is built upon a multiyear dataset obtained from TU Varna meteorological station between 2012-2016. A significantly higher density distribution of Clear sky segments was identified during the summer through the Clear Sky Identification algorithm. To avoid the risk of overfitting the models to purely summer months and poor model fits in winter months, which was found to be the case with the legacy model, the underrepresented clear sky regions (based on θ) were replicated until uniform distribution is attained. Subsequently, a genetic optimization was applied to selected parameters in the Clear Sky algorithms and the updated models showed a significant improvement in low winter months (θ) and even overall performance boost RMSE / MAE /R2. Furthermore, such validations and optimizations are recommended prior to any design or real-time PV-system analysis for the specific location.