Parameterized optimization problems (POPs) belong to a class of NP problems which are hard to be tackled by traditional methods. However, the relationship of the parameters (usually represented as k) makes a POP different from ordinary NP-complete problem in designing algorithms. In this paper, GEAS, an evolutionary computing algorithm (also can be seen as a framework) to solve POPs is proposed. This algorithm organically unifies genetic algorithm (GA) framework and the idea of evolutionary strategy (ES). It can maintain diversity while with a small population and has an intrinsic parallelism property:each individual in the population can solve a same problem that only has a different parameter. GEAS is delicately tested on an NP-complete problem, the Critical Link Set Problem. Experiment results show that GEAS can converge much faster and obtain more precise solution than GA which uses the same genetic operators.