Multidimensional scaling (MDS) is well known technique for analysis of multidimensional data. The most important part of implementation of MDS is minimization of STRESS function. The convergence rate of known local minimization algorithms of STRESS function is no better than superlinear. The regularization of the minimization problem is proposed which enables the minimization of STRESS by means of the conjugate gradient algorithm with quadratic rate of convergence.