Local sensitivity coefficients and parameter uncertainties are commonly used to guide the selection of parameters in the optimization of combustion kinetic models. In this study, we proposed two additional factors that influence parameter importance and can be utilized for active parameter selection in optimization workflows: the experimental uncertainty and the deviation between experimental and simulated results. Based on these factors, we proposed four novel impact measures for parameter ranking. These were benchmarked against two commonly used methods by constructing hierarchical optimization strategies of a methanol/NOx mechanism involving ten parameters and a large experimental dataset. Results showed that measures which did not incorporate uncertainty information led to the steepest initial reduction in the error function. However, over the full course of optimization, methods considering uncertainty achieved better overall performance, despite following similar improvement trends. Among all evaluated strategies, one of the newly introduced measures - based on error function derivatives and uncertainty information - demonstrated the most consistent performance in reducing posterior uncertainties.