Code optimization involves the application of rules and algorithms to program code, and its main objective is to run the code faster with lesser memory. But achieving this target involves lot of complication because arriving at the compiler configuration for a particular problem is a complex process. The performance of the program measured by time and memory depends on the machine architecture, problem domain and the settings of the compiler. There have been several proposed techniques that search the space of compiler options to find solutions. However such approach can be expensive. In current compilers, through command line arguments, the user must decide which optimization is to be applied in a given compilation run. But it is not a long term solution. Because compiler optimizations get increasingly numerous and complex, this problem must find an automated solution. ln this paper, it is proposed to study the classification of problems, identification of ideal objective functions for different tasks and the ordering of objective function for optimization. In this paper we proposed an effective orchestration algorithm to select best set for a particular problem from larger set options. Many previous works consider only limited set of options. In this paper, we implemented compiler optimization selection algorithms such as branch and bound strategy and advanced combined elimination algorithm and evaluated its execution speed up. We argue that advanced combined elimination algorithm works better when compared to branch and bound strategy by showing experimental results using benchmark applications.