Local and global optimization algorithms are used commonly in geophysical data inversion. Each type of algorithm has unique advantages and disadvantages. Here we propose several methods of combining the two algorithms such that we can overcome their drawbacks and make use of the salient features of the two methods. In particular, we combined a local conjugate gradient (CG) method with a global very fast simulated annealing (VFSA) approach to solve problems of geophysical interests. We conducted a systematic study to find an efficient strategy to combine CG and VFSA optimization schemes and recommend a couple of ways for future implementations. Seven different hybrid algorithms were first tested on a set of field 1-D Schlumberger resistivity sounding data and their performances were compared with stand-alone genetic algorithm (GA), simulated annealing, and local search algorithms. Almost all of the proposed hybrid algorithms were found to be computationally more efficient than the conventional global optimization approaches. Having found the most efficient of the hybrid approaches we apply them to the problem of seismic velocity analysis using seismograms recorded in the offset-time domain. Finally, we applied the hybrid algorithm to a 2-D field resistivity profiling data collected over a disseminated sulfide zone at Safford Arizona and compared our hybrid inversion results with the previously published results.