An adaptive monkey algorithm (AMA) used for optimal sensor placement (OSP) is proposed to solve the problem that the search methods of the climb process and watch-jump process are mechanic and the pattern of the somersault process is single. Firstly, the dual-structure coding method is utilized to overcome that the original monkey algorithm could only perform the optimization for the continuous variables. Then, the climb process and watch-jump process are updated in order to adaptively select the two search methods, which can improve the local search ability and efficiency of the algorithm. In addition, the two new somersault processes, i.e., reflection somersault process and variation somersault process, are introduced to strengthen the global search ability of the algorithm. Finally, taking the Dalian international trade mansion as an example, the parametric sensitivity analysis and selection of the OSP scheme are performed. The results show that the AMA can better work out the OSP and the search efficiency has been greatly improved compared to the original algorithm.