This paper addresses the complex optimization problem, of which the objective function consists of two parts: One part is differentiable and the other part is non-differentiable. Accordingly, we decompose the original objective function into several relatively simple sub-objective ones, which subsequently formulate as a multiobjective optimization problem (MOP). To solve this MOP, we propose a simulated water-stream algorithm (SWA) inspired by the natural phenomenon of water streams. The water streams with a hybrid process of downstream and penetration towards the basin is analogous to the process of finding the minimum solution in an optimization problem. The SWA featuring a combination of deterministic search and heuristic search generally converges much faster than the existing counterparts with a considerable accuracy enhancement. Experimental results show the efficacy of the proposed algorithm.