In this study, by using an observing system simulation experiment, a shuffled complex evolution algorithm (SCE-UA) is evaluated in terms of the effectiveness and efficiency of calibrating parameters in one-dimensional Richards equation, including soil components and all physical parameters. The result shows that the ability of calibrating parameters with SCE-UA depends on not only the uncertainty ranges of parameters but also their sensitivity degrees. For sensitive parameters, unique optima parameter estimates can be easily obtained. However, for insensitive ones, there exists an "insensitive range", and only suboptimal parameters are obtained in this range. By increasing training times and reducing the parameters' range, the performance of parameter calibration in the insensitive range can beimproved. Moreover, the overdetermination of the model parameters may result in suboptimal parameter estimates, and a good calibration effectiveness can be archived by appropriately setting parameters and adding constrains and criterions.