Reservoir parameter inversion is an essential geophysical problem to quantify the volume of an oil and gas reservoir. This problem can be identified as a nonlinear multiparameter optimization problem, in which the rock-physics model links seismic measurements including the velocity of the P- and S-waves to reservoir properties including porosity, water saturation, and permeability. Due to the complexity of the rock-physics and the nonlinear relationship, local search methods fail if the starting model is too far from the true model. Stochastic methods such as the genetic algorithm have global search ability, but they suffer from weak local search ability and premature convergence. We have developed a novel hybrid genetic algorithm to overcome these drawbacks of conventional genetic algorithm. We have used concepts of the self-adaptive method and evaluation criteria from the simulated annealing method to develop a new self-adaptive selection operator and a new self-adaptive hybrid crossover operator. With the two new operators, the hybrid genetic algorithm has better local search ability and is able to prevent premature convergence. We applied the hybrid genetic algorithm to the reservoir parameter inversion problem, and we used the Biot-squirt model as a rock-physics model. Numerical experiments demonstrated the improved search performance of the hybrid genetic algorithm against conventional methods in terms of solution quality, convergence rate, and convergence speed. We also tested our method on a series of laboratory and field data, which also confirmed the effectiveness of our method.