In this work, we have proposed an amplitude reordering method which can generate a quasioptimal order for a collection of operators of a given parameterized state. We have found that by introducing the AR method into the ADAPT-VQE algorithm, the number of adaptive iterations, required in the simulation can be significantly reduced. In order to strengthen this conclusion, in addition to the numerical calculations using (AR-)ADAPT-VQE as a benchmark, we have also performed numerical tests by using (AR-)AES-VQE. Our numerical simulations have shown that the incorporation of the AR method indeed reduces the total number of measurements by a factor, and this reduction factor hopefully increases as the length of final Ansätze grows, meanwhile it generally does not sacrifice the accuracy and not increase the circuit depth. Although only two kinds of adaptive algorithms have been tested in this work, we are sure that the AR strategy is general enough to improve other adaptive algorithms like Qubit-ADAPT-VQE 30 in principle, because the AR method has little requirements to the specific form of the operators if only they can be represented as an operator exponential function, e θp Tp , and can be sorted by some indicators I p . For the same reason, AR can also be combined with various scaling-reducing Hamiltonian sampling methods, to further reduce the number of measurements and time cost in VQE simulations.