The constant parameters in deterministic optimization comprise a great deal of uncertainty, significantly affecting the outcome of optimization research. Therefore, the analysis and research of designing and implementing optimization techniques for the parameter uncertainty provides a better understanding of the process. The approach is to evaluate the parameters randomly within the specific range of ± 10%, where the distribution data for uncertain parameters is obtained from literature, and solve by converting the uncertain optimization problem into a deterministic one. Although polylactide optimization improves the overall polymer efficiency, the parameter uncertainty involved in the process is inevitable. Hence, expected value-based methodology is proposed to analyze the uncertainty associated with kinetic parameters in lactide Ring-Opening Polymerization with the objective functions: maximize the number average molecular weight versus minimize molecular weight distribution and minimize time. Expected Value Non-Dominated Sorting Genetic Algorithm (E-NSGA-II) is used for the uncertainty analysis. The study identified activation rate, deactivation rate, and chain scission rate as the most sensitive kinetic parameters.