A reliability-based multidisciplinary optimization framework is constructed by coupling high-fidelity commercial solvers for aeroelastic analysis and an in-house code developed for reliability analysis. The finite volume-based flow solver Fluent is used to solve inviscid three-dimensional Euler equations, whereas three-dimensional solid models are updated using Catia parametrically. A mesh-based parallel code coupling interface (MPCCI), is used to exchange the pressure and displacement information between Fluent and Abaqus to perform a loosely coupled aeroelastic analysis. The optimization criteria include both deterministic and probabilistic constraints with both structural and aerodynamic uncertainties, such as in yield strength, Mach number, and angle of attack. To evaluate the probability of failure for the probabilistic constraints a first-order reliability analysis method, the Hasofer-Lind iteration method, is implemented in MATLAB to compute the most probable failure point solution. The integrated framework is validated with structural problems and then extended to more realistic wing configurations with aeroelastic criteria. The presented reliability-based multidisciplinary optimization process is proven to be fully automatic, modular, and practical, which could find potential applications in industrial problems.