Global optimization with unknown Lipschitz constant We study the global optimization problem i.e. for a real valued and bounded function f we are interested in a point x of the domain whose function value f(x) is close to the infimum inf f. We consider the case that f is d variate Lipschitz and in a certain sense does not increase too slowly in a neighborhood of the global minimizer(s). We give two contributions: We show that for an optimal method adaptiveness is necessary and that randomization (Monte Carlo) yields no further advantage. We present a method that is universal in the following sense: This algorithm has the optimal rate of convergence even if neither the Lipschitz constant nor any other function parameter is known.