Model-based deep learning (MoDL) frameworks, which combine deep learned priors with imaging physics, are now emerging as powerful alternatives to compressed sensing in a variety of reconstruction problems. In this work, we investigate the impact of sampling patterns on the image quality. We introduce a scheme to jointly optimize the sampling pattern and the reconstruction network parameters in MoDL scheme. Experimental results demonstrate the significant improvement in reconstruction quality with sampling optimization. The results also show that the decoupling between imaging physics and image properties in MoDL offers improved performance over direct inversion scheme in the joint optimization scheme.