The individual-discipline-feasible (IDF) formulation was proposed to simplify the implementation of MDO problems. The IDF formulation introduces coupling variables into the optimization problem that eliminate the need for a full multidisciplinary analysis at each optimization iteration; this simplifies the solution of MDO problems by maintaining modularity of the discipline software. Historically, the MDO community has used conventional optimization algorithms to solve IDF-formulated problems. Conventional optimizers are not well suited to IDF, because they use limited-memory quasi-Newton methods (linear convergence) and require the constraint Jacobian explicitly. The cost of computing the coupling-variable constraint Jacobian is prohibitively expensive for high-fidelity IDF problems. Matrix-free Reduced-Space inexact-Newton-Krylov (RSNK) algorithms overcome these issues, because they scale superlinearly and do not require the constraint Jacobian explicitly. Therefore, this class of algorithm has great potential to solve IDF-formulated MDO problems in a scalable and efficient manner. In this paper, we describe the application of RSNK to the IDF formulation and compare its performance to the multidisciplinary feasible architecture.