We present a new approach to treatment assignment in (field) experiments for the case of one or multiple treatment groups. This approach - which we call minimizing MSE approach - uses sample characteristics to obtain balanced treatment groups. Compared to other methods, the min MSE procedure is attrition tolerant, more flexible and very fast, it can conveniently be implemented and balances different moments of the distribution of the treatment groups. Additionally, it has a clear theoretical foundation, which bases on the idea by Kasy (2016), but involves randomness, works without any parameter to be specified by the researcher and is extended to multiple treatments. The information used for treatment assignment can be multivariate and continuous and consist of arbitrary many variables. In this paper, we theoretically derive the underlying selection criteria which we then apply to various simulated treatment effect scenarios and datasets, comparing it to established approaches. Our proposed method performs superior or comparable to competing approaches such as matching in most measures of balance commonly used. We provide software to apply the min MSE approach as an ado-package for Stata.