This talk discusses the challenging problem of finding global optimal solutions for two-stage stochastic programs with continuous decision variables and nonconvex recourse functions. We introduce a two-phase approach, which does not only generate global lower bounds for the nonconvex stochastic program but also simplifies the computation of the expected value of the recourse function by using moments of random vectors. This makes our overall algorithm particularly suitable for the case where the random vector follows a continuous distribution or when dealing with many scenarios. Numerical experiments are given to demonstrate the effectiveness of our proposed approach.