Biogas produced during the anaerobic degradation of the organic matter from waste and wastewater has become an important source of renewable energy. Mathematical modeling can help to predict and control the system behavior in order to assure process stability and biogas production maximization. The identifiability of anaerobic digestion models presents serious difficulties mostly due to the lack of reliable measurements, usually only the biogas flow. In this study, an assessment of the optimization of an anaerobic digestion model in a batch test is presented based on the application of global sensitivity analysis, multi-start optimization procedure and parameters uncertainty estimation. In regards to the experimental information two different conditions that can be actually manipulated, were assessed: The initial conditions in terms of substrate and biomass in the batch test and the sampling frequency of the biogas flow. The results indicate that if methane is considered as the sole experimental output, the variations of the initial conditions regarding the substrate and inoculum in a batch test does not improve the models parameters sensitivity in a significant way. Adding some off-line measurement of another variable, such as a substrate concentration, will enhance the parameters sensitivity. In terms of the optimization procedure, the frequency of the sampling (on-line or off-line) exert a significant influence specially in regards to dodging to get trapped in local minima. The use of a multi-start strategy is strongly recommended since it gives a more clear and wide vision of the optimization results by sweeping a large range of possible initial guesses. Both the Fisher information matrix and the Hessian may yield different results depending on the complexity of the model, the parameters to be identified and the minimization criteria chosen for the optimization. Further research on optimization of anaerobic digestion models is encouraged.