Correlation as an evaluation metric for regression
The organizers might deem the direction of predicted change more important than the magnitude, i.e., it is more important that your prediction is high when the known value is high (and vice versa) than to get as close to the known value as possible. The measurements might be noisy anyway.

One, fairly robust way of optimizing for it would be by grid-searching for the local optimum, like in this QA.

However, you should also take note that algorithms tweak ther internal parameters when fitting according to some loss function. Some algorithms accept custom cost functions and derivatives, yet some implementations don't. Information-theoretic measureas are standard in classification while MSE in regression.

Theoretically, you should be able to tell your Random Forest (or another algorithm accordingly) what the optimal split is as a function of pearson correlation.