In this work, we propose a novel way to maximize the number of bugs found for black-box mutational fuzzing given a program and a seed input. The major intuition is to leverage a white-box symbolic analysis on an execution trace for a given program-seed pair to optimize parameters for mutational fuzzing. The result is promising: we found 25% more bugs than the state- of-the-art fuzzers over 8 applications, given a limited resource. We make our code publicly available to foster open science.