How to decide the number of trees parameter for Random Forest algorithm in PySpark MLlib?
Maybe you will start to have negative impact of performance by increasing the number of trees. But definitely, at some point, increasing the number of trees will not add much accuracy in your model. 

The usual way is to perform a k-fold cross-validation for different number of trees (and any other combination of model's parameters) and choose the one with the best performance. If you have any question about cross-validation, check this page. Instead of using simple accuracy, try to use other metrics that take into account the complexity of the model, like AIC.

Obs.: the final accuracy of the algorithm should be calculated using a separate dataset, not used in the cross-validation (usually called test set).