Over the last decades, scanning magnetic microscopy techniques have been increasingly used in paleomagnetism and rock magnetism. Different from standard paleomagnetic magnetometers, scanning magnetic microscopes produce high-resolution maps of the vertical component of the magnetic induction field on a plane located over the sample. These high-resolution magnetic maps can be used to estimate the magnetization distribution within a rock sample by inversion. Previous studies have estimated the magnetization distribution within rock samples by inverting the magnetic data measured on a single plane above the sample. A recent work presented a spatial domain method for inverting the magnetic induction measured on four planes around the sample in order to retrieve its internal magnetization distribution. This methodology presumes that the internal magnetization distribution of the sample varies along one of its axes. Moreover, the sample geometry can be approximated by an interpretation model composed of a one-dimensional array of juxtaposed rectangular prisms with uniform magnetization. The Cartesian components of the magnetization vector within each rectangular prism are the parameters to be estimated by solving a linear inverse problem. This method takes an advantage on dealing with the averaged magnetic field due to the finite size of the magnetic sensor, preventing the application of a deconvolution before the inversion. Tests with synthetic data show the advantage of inverting the magnetic data on four planes around the sampleand how this new acquisition scheme improves the estimated magnetization distribution within the rock sample. However, we have to analyze the influence of the scanning geometry and the number of measurements on each observation plane. In this work, we propose some strategies on determining optimal acquisition design and sensor-to-sample distance in order to improve practical use of this method based on Statistical Experimental Design. We applied this technique for constructing a Quality Factor map that defines previuosly an experimental procedure to set the two main acquisition parameters. Tests with synthetic data simulating a ferromanganese crust show the robustness of this technique on providing a supplementary tool to optimize the acquisition design of the method.