We present a global strategy for molecular simulation forcefield optimization, using recent advances in Efficient Global Optimization algorithms. During the course of the optimization process, probabilistic kriging metamodels are used, that predict molecular simulation results for a given set of forcefield parameter values. This enables a thorough investigation of parameter space, and a global search for the minimum of a score function by properly integrating relevant uncertainty sources. Additional information about the forcefield parameters are obtained that are inaccessible with standard optimization strategies. In particular, uncertainty on the optimal forcefield parameters can be estimated, and transferred to simulation predictions. This global optimization strategy is benchmarked on the TIP4P water model. © 2013 Wiley Periodicals, Inc.