The M-tree is a dynamic data structure designed to index metric datasets. In this paper we introduce two dynamic techniques of building the M-tree. The first one incorporates a multi-way object insertion while the second one exploits the generalized slim-down algorithm. Usage of these techniques or even combination of them significantly increases the querying performance of the M-tree. We also present comparative experimental results on large datasets showing that the new techniques outperform by far even the static bulk loading algorithm.