Order of hyperparamater tunning
Since the various hyperparameter are related you cannot be sure that - for instance - a tuned value for min_samples_split from a model where all other hyperparameter are set to default values, will be generally optimal. When you have a situation where you limit the depth of trees (by max_depth assuming you use sklearn), min_samples_split may be a non-binding constraint. It may however be relevant when you set max_depth=None, since as stated in the docs:

If None, then nodes are expanded until all leaves are pure or until
all leaves contain less than min_samples_split samples.

From a practical perspective I would start tuning n_estimators (more tend to be better), max_depth, and max_features (square root of the number of features is a good starting point) with a fixed random_state. Contingent on the reults you may start adding additional constraints.