In the magnetic flux leakage (MFL) detection process of oil pipelines, the received MFL signals have a relatively low signal-to-noise ratio (SNR) and are often aliased with other low-frequency noises that have similar frequency components. Currently, common denoising algorithms cannot effectively separate MFL signals from other noise components. To improve the accuracy of MFL signal extraction, this paper proposes a method combining variational mode decomposition (VMD) optimized by the giraffe optimization algorithm (GOA) and dynamic wavelet threshold denoising (DWTD) for MFL signal identification.Firstly, the GOA algorithm is used to select the input parameters of VMD. Then, VMD performs adaptive decomposition on the signal based on the optimized parameters to obtain a certain number of modal components, which are divided into effective signal components and noise components using correlation coefficients.Finally, dynamic wavelet threshold denoising is applied to the effective signal components to obtain the noise-removed MFL signals.Tests and analyses were conducted on simulated signals with different intensities and actual measured MFL signals. The index analysis of simulated signals shows that the SNR of the proposed method is at least 20% higher than that of other methods, and the smoothness of actual measured MFL signals is 10% higher than that of other methods. The results indicate that the denoising method combining GOA-optimized VMD parameters and DWTD can effectively remove interference noise and retain the original signal waveform more completely. This method outperforms other commonly used denoising methods and is suitable for denoising MFL signals of oil well pipelines.