Compiling source code effectively to meet various criteria is a critical task in software engineering. Especially, code size reduction has attracted much attention from both industry and academia due to the requirement of resource utilization. Generally, developers rely on compiler optimization passes to realize code size reduction. However, it is impractical to select a desirable optimization sequence manually since a wide variety of optimization passes are integrated into a compiler. Evolutionary algorithms offer an impressive way to alleviate this problem. Nevertheless, previous approaches fail to balance the exploitation and exploration of the search space. Moreover, the expensive fitness evaluation requires actual compilation, which makes the evolution rather time-consuming. To tackle the challenges, we propose a novel approach SMARTEST, which characterizes the systematic exploitation of a huge volume of historical compilation information. Specifically, SMARTEST comprises two components: 1) a local search operator to enhance the solution quality; and 2) a data-driven surrogate model to avoid expensive fitness evaluation. We evaluate the effectiveness of SMARTEST over the cBench benchmark suite. Experimental results indicate that SMARTEST outperforms the standard level -Os by 2.17% on average, and achieves 1.2 times code size reduction compared with the genetic algorithm. Furthermore, experimental results over the benchmark suite evidently show that SMARTEST gets a better result and takes less actual fitness evaluations than its variants, which demonstrates the contribution of the local search and the surrogate model.