Effective computational methods are important for practitioners and researchers working in strategic underground mine planning. We consider a class of problems that can be modeled as a resource-constrained project scheduling problem with optional activities; the objective maximizes net present value. We provide a computational review of math programming and constraint programming techniques for this problem, describe and implement novel problem-size reductions, and introduce an aggregated linear program that guides a list scheduling algorithm running over unaggregated instances. Practical, large-scale planning problems cannot be processed using standard optimization approaches. However, our strategies allow us to solve them to within about 5% of optimality in several hours, even for the most difficult instances. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Funding: This work was supported by Alford Mining Systems, the Centro de Modelamiento Matemático [Grants ACE210010 and FB21005], ANID-Chile [BASAL funds for center of excellence and FONDEF Grant ID19-10164], and the supercomputing infrastructure of the NLHPC [Grant ECM-02].