This paper presents a path planner for rendezvous of multiple autonomous underwater vehicles (AUVs) in turbulent, cluttered, and uncertain environments. The proposed strategy combines an Optimized Mass-center rendezvous point selection scheme with an evolutionary path planner to find trajectories for multiple AUVs with minimal time usage over all participating vehicles and simultaneous arrival of the vehicles at their selected rendezvous destination. A quantum-behaved particle swarm optimization (QPSO) algorithm is used with a cost function which is determined by the sum of time usage over all participating vehicles accounting for the effect of space-time variable currents and the sum of the waiting time of every vehicle. The proposed path planner is tested to generate optimal trajectories for rendezvous of multiple AUVs navigating through a variable ocean environment in the presence of irregularly shaped terrains as well as obstacles whose position coordinates are uncertain. Simulation results show that with integration of the Optimized Mass-center rendezvous point selection scheme, the proposed methodology is able to obtain more optimized trajectories for multiple AUVs than the ones relying on centroid, mass-center or optimized full-scale rendezvous point selection schemes.