The talk will discuss the strong approximation of stochastic differential equations with respect to the global error in the L2-norm as well as in the uniform norm. For equations with additive noise we present sharp lower and upper bounds for the minimal error in the class of arbitrary methods which use a fixed number of observations of the driving Brownian motion. We introduce Euler schemes with adaptive step-size control which perform asymptotically optimal.