Abstract A single method that does not consider all performances of mode testing is typically inadequate for determining the optimal sensor placement. However, if different sensor placement methods are applied together, the multi-objective optimization problem will incur high computational costs. Although transforming multi-objective optimization into single-objective optimization by defining weight factors is convenient, this artificial setting disturbs the inherent characteristics of different methods in combined optimization. To overcome these shortcomings in solving a multi-objective problem for sensor locations, a sensor placement algorithm for structural health monitoring based on an iterative updating process is proposed. This method can be applied to different structures owing to the use of adaptive weight factors in the combined objective. In this study, considering different optimal sensor placement methods from their own perspectives, a novel combined fitness function using weight factors and normalization is constructed and solved by a genetic algorithm. Instead of comparison formats, first, the equivalent formats of six well-known sensor placement methods are used for optimization. Considering the effects of the order differences of different objectives, the multi-objective function is transformed into a single-objective optimization problem. Furthermore, an adaptive algorithm using an iterative process involving weight-factor updating is established; thus, the influence of the disturbance originating from directly deciding the weight factor is reduced to the greatest extent. The weight-factor updating process that allows this algorithm to achieve high accuracy and rapid convergence is described in detail. Finally, three engineering numerical examples are considered to demonstrate the effectiveness and feasibility of the proposed algorithm under five sensor placement criteria, including the sensor distribution index and the ratio of the same positions.