Magnetization transfer contrast MR fingerprinting (MTC-MRF) is used to quantify multiple tissue parameters of free bulk water and semisolid macromolecule using pseudo-randomized MRF schedules. An optimal design of the MRF schedule is important to improve the quantification accuracy and reduce the scan time. However, lack of the objective function that represents the encoding capability of MRF schedule hinders the reliable optimization. In this study, we propose a novel metric that represents the encoding capability of MRF schedule based on recurrent neural network. Unlike the conventional metrics based on indirect measurements, the proposed learning-based metric directly measures the tissue quantification errors.