Quantitative sodium MRI could be a sensitive tool for therapy monitoring in muscular diseases. However, sodium MRI suffers a low signal-to-noise ratio (SNR). 3D dictionary-learning compressed-sensing (3D-DLCS) enables SNR improvement and acceleration of sodium MRI, but it is dependent on parameterization. In this work a simulation based optimization method for 3D-DLCS is presented, which finds the most suitable parameters for 3D-DLCS in the context of sodium quantification. The method is applied in an in vivo study to quantify sodium in the skeletal muscle. The optimized 3D-DLCS yields a lower quantification error than the reference reconstruction method (Nonuniform FFT).