QU-fitting is a standard model-fitting method to reconstruct distribution of magnetic fields and polarized intensity along a line of sight (LOS) from an observed polarization spectrum. In this paper, we examine the performance of QU-fitting by simulating observations of two polarized sources located along the same LOS, varying the widths of the sources and the gap between them in Faraday depth space, systematically. Markov Chain Monte Carlo (MCMC) approach is used to obtain the best-fit parameters for a fitting model, and Akaike and Bayesian Information Criteria (AIC and BIC, respectively) are adopted to select the best model from four fitting models. We find that the combination of MCMC and AIC/BIC works fairly well in model selection and estimation of model parameters in the cases where two sources have relatively small widths and a larger gap in Faraday depth space. On the other hand, when two sources have large width in Faraday depth space, MCMC chain tends to be trapped in a local maximum so that AIC/BIC cannot select a correct model. We discuss the causes and the tendency of the failure of QU-fitting and suggest a way to improve it.