This paper is concerned with the optimal approximation of a given multivariate Dirac mixture, i.e., a density comprising weighted Dirac distributions on a continuous domain, by an equally weighted Dirac mixture with a reduced number of components. The parameters of the approximating density are calculated by minimizing a smooth global distance measure, a generalization of the well-known Cram\'{e}r-von Mises Distance to the multivariate case. This generalization is achieved by defining an alternative to the classical cumulative distribution, the Localized Cumulative Distribution (LCD), as a characterization of discrete random quantities (on continuous domains), which is unique and symmetric also in the multivariate case. The resulting approximation method provides the basis for various efficient nonlinear state and parameter estimation methods.