To ensure the statistical result validity, model-data fit must be evaluated for each item. In practice, certain actions or treatments are needed for misfit items. If all misfit items are treated, much item information would be lost during calibration. On the other hand, if only severely misfit items are treated, the inclusion of misfit items may invalidate the statistical inferences based on the estimated item response models. Hence, given response data, one has to find a balance between treating too few and too many misfit items. In this article, misfit items are classified into three categories based on the extent of misfit. Accordingly, three different item treatment strategies are proposed in determining which categories of misfit items should be treated. The impact of using different strategies is investigated. The results show that the test information functions obtained under different strategies can be substantially different in some ability ranges.