The trend towards sensor miniaturization has heightened interest in optimizing sensor array configurations across various scientific and industrial applications that imply multichannel measurements. In this article, we present an extended version of our previous approach, adapting it to the realworld conditions in magnetoencephalography (MEG). At each step the method selects the sensor location that maximizes the ROI-related signal-to-noise ratio (SNR), followed by the projection recursively applied to the gain matrix to orthogonalize the subsequent stereotypic steps with respect to the source subspace served by the already selected sensors. This algorithm offers high computational efficiency and flexibility with respect to physical constraints. We dub our approach as Optimal Layout Design via Recursively Applied Leadfield Elimination (OLD RALFE). Our analysis of the optimized MEG sensor layouts obtained with OLD RALFE shows that the proposed algorithm performs similarly to several other significantly more computationally demanding sensor layout optimization approaches in terms of both SNR and the Cramér-Rao lower bound (CRLB) on the spatial resolution of the inverse problem solution. OLD RALFE demonstrated in application to MEG, is broadly applicable to many other multichannel measurement challenges.