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Cryosurgery is becoming an effective method to treat undesired tumor tissue via the application of extremely low temperature coolant flowing through a probe. To maximize cryoinjury in the targeted tumor region, multiple cryoprobes are simultaneously employed to freeze the surrounding tumor tissues. In order to minimize cryosurgical injury to the surrounding health tissues, the cryoprobe localization for each specific tumor is critical. We developed finite difference analysis to calculate the freezing process during cryosurgery. According to our experimental data, the simulation results from the Pennes equation coupled with immersion boundary method (IBM) are consistent with the experimental data, with a maximum error of ± 8.12 %.We further incorporate a genetic algorithm as a planning tool to address the problem in connection to be interactive positioning of the cryoprobes and the freezing process duration during the pre-operative stage. Several optimization scenarios are judiciously investigated to emphasize different performance variables. Key results have revealed that this computerized approach is highly efficient one for selecting optimized multiple operational parameters.