Land surface models involve a large number of interdependent parameters that affect the physics of how surface energy fluxes are partitioned between latent heat, sensible heat, net radiative, and ground heat fluxes. The goal of an optimal parameter and uncertainty analysis of a land surface model is to identify a range of parameter sets that enable model predictions to be bounded within observational uncertainties. Here we apply Bayesian stochastic inversion (BSI) using very fast simulated annealing (VFSA) to identify parameter sets of the Chameleon surface model (CHASM) land surface model that are consistent with the uncertainty limits ascribed to a high-quality data set collected from Cabauw, Netherlands. These results are compared to the parameter sets obtained through the multicriteria (MC) approach. All analyses evaluate model performance against daily and monthly mean observations of sensible, latent, and ground heat fluxes. BSI and MC identify similar best fit model parameter sets that improve CHASM performance over default parameter settings. The three most important CHASM parameters at Cabauw are minimum stomatal resistance, vegetation roughness length, and vegetation fraction cover. BSI is based on a Bayesian inference model such that that it expresses uncertainty in terms of a posterior probability density function, different moments of which provide information about parameter means and covariances. Although MC gives a range of possible optimal parameters through the concept of a Pareto set, we found that these ranges did not provide a consistent or representative view of the uncertainty within the observational data. The BSI algorithm in the current study is particularly efficient in that it only requires about double the number of model evaluations than the MC algorithm. This is a substantial saving over other more accurate methods to evaluate uncertainty such as the Metropolis/Gibbs' sampler that requires at least 40 times more computations than the BSI algorithm to obtain similar results.