This talk discusses geometric programs with joint probabilistic constraints. When the stochastic parameters are normally distributed and independent of each other, we approximate the problem by using piecewise linear functions, and transform the approximation problem into a convex geometric program. We prove that this approximation method provides a lower bound. Then, the convexity and solution method for geometric programs with joint rectangular probabilistic constraints are discussed. Numerical tests are carried out on a stochastic shape optimization problem.