The optimal location and the number of sensors in the wireless sensor network were found efficiently and accurately using an improved genetic algorithm (IGA) and applied to the dynamic detection of bridges. First, we optimized the conventional genetic algorithm (GA) by considering the optimization characteristics of multiple sensors. IGA improves the drawbacks of the conventional GA, such as slow convergence and the tendency to fall into local optima when applied to large structures. This improvement increases the convergence speed and ensures an adequate search for the optimal value. To achieve the optimal arrangement, classical optimization criteria including the acceptable independence, model confidence, and model strain energy criteria are embedded into the IGA as fitness functions. Through the simulation analysis of a bridge model, we demonstrate that the IGA outperforms the conventional GA in terms of searching ability, computational efficiency, reliability, and other relevant metrics. Moreover, the IGA significantly outperforms the classical sequence method in the searching ability.