Both Entropy and Gini can be sensitive to unbalanced data, as the value for the node purity is based off of the proportion of observations in the node with the different response levels. Usually this is a larger problem in rare event modeling. One outcome group being twice the size of another is not as likely to be a large issue.
Additionally, CHAID and FastCHAID both should be less sensitive to the data with imbalanced numbers of each outcome group than Entropy and Gini. That being said, if the imbalance is too large, it might be better practice to oversample the data beforehand.
If you have the time and the arrangement, I would recommend building several decision trees using different criterion, and then use validation data to determine the best tree.
