Which hyperparameters are important in classification trees?

I am trying to tune a CART classifier, and am overwhelmed at the hyperparameter optimization. There are just so many, and I don't know which ones to optimize. You have at least these parameters:

• maximum depth of tree
• minimum number of observations in a node to attempt another split
• minimum number of observations in any terminal leaf node
• minimum factor by which lack of fit has to be decreased to execute the next split

I don't think I can do all of them, this is just too expensive on CPU time even for smaller grids.

Is there a consensus which hyperparameters to optimize in classification trees?