We propose an algorithm, called orthogonalizing EM (OEM), intended for fitting penalized regression models.It is motivated by the experimental design problem of fitting penalized regression with an orthogonal design with missing data.In each iteration, the algorithm imputes the missing data based on current estimators of the parameters and computes the penalized regression problem for the underlying orthogonal design, which often has a closed-form solution.