When new wells are planned, typically the same trajectory is used for assessing the effectiveness of conventional wells and wells with smart completions. This study demonstrates that the economically optimized trajectory for smart and conventional wells can be very different. Two new well trajectory optimization algorithms were developed using Stochastic Pattern Search (SPS) principles. In both algorithms, random perturbations are made starting from an initial well trajectory, which are sent to a reservoir simulator. Thereafter the perturbation with the highest Net Present Value (NPV) is selected. New perturbations of the selected well trajectory are made and simulated to, again, select the highest NPV. This process is repeated until a certain stopping criterion is met. The two methods differ in the selection of the perturbations used to initiate the new iteration and have a slightly different computational performance. To demonstrate the difference between the optimal well trajectory of a well with a conventional and a smart completion, both the SPS1 and SPS2 method were evaluated using a realistic, but slightly simplified reservoir model. Both methods were able to optimize the trajectory for both conventional and smart completions. The SPS1 method quickly converged to a local optimum, whilst the SPS2 method was able to determine a trajectory with a significantly higher NPV for both the conventional and smart wells. Moreover, the optimal well trajectory with the smart completion, as found by the SPS2 algorithm, had an NPV that was 40% higher than the smart well with the trajectory which was optimal for the conventional completion. Following the above, it can be concluded that when smart completions are assessed, well trajectory (re)optimization can have a very significant value impact and may be crucial in evaluating the full potential of the completion. Furthermore, it was shown that, for the investigated case, the SPS2 procedure is a good method for well trajectory optimization in a three-dimensional reservoir and, although more testing is needed, it is believed that it has potential to work with any type of completion.